ArticlePDF Available

Evolving Electric Mobility: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

January 2024
IEEE Access PP(99):1-1

DOI:10.1109/ACCESS.2024.3356598

License
CC BY-NC-ND 4.0

Authors:

Syed Shehryar Ali Naqvi

Jeju National University

Harun Jamil

Jeju National University

Show all 7 authorsHide

The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU), Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines, emphasizing development approaches and algorithms specifically designed for mobility energy efficiency. The study shows the effect of various control units on energy efficiency by carefully examining case studies and real-world applications. Employing a critical assessment, the paper examines the advantages and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their effectiveness in various situations. The presented study is typically related to the relationship between ECUs and their sub-branches through an integrated approach. By comparing current control approaches, the study offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating knowledge of multiple control areas in EV. The research effort will give researchers a path forward and make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area of integrated ECUs in EVs.

Available via license: CC BY-NC-ND 4.0

Content may be subject to copyright.

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Digital Object Identifier 10.1109/ACCESS.2023.0322000

Evolving Electric Mobility: In-Depth Analysis of

Integrated Electronic Control Unit Development

in Electric Vehicles

SYED SHEHRYAR ALI NAQVI1, HARUN JAMIL1, NAEEM IQBAL2, SALABAT KHAN3, MURAD ALI

KHAN4, FAIZA QAYYUM4, and DO-HYEUN KIM1,4,*

1Department of Electronics Engineering, Jeju National University, Jeju, 63243, Republic of Korea, syedshehryar@stu.jejunu.ac.kr, 1harunjamil@gmail.com

2School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT7 1NN Belfast, U.K, n.iqbal@qub.ac.uk

3(Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan) (Big Data Research Center, Jeju National

University, Jeju 63243, Republic of Korea)

4Department of Computer Engineering, Jeju National University, Jeju, 63243, Republic of Korea, muradali@stu.jejunu.ac.kr, faizaqayyum@jejunu.ac.kr

Corresponding author: D.H. Kim (kimdh@jejunu.ac.kr).

This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government.

[23ZD1160, Regional Industry ICT Convergence Technology Advancement and Support Project in Daegu-GyeongBuk (Mobility)

ABSTRACT

The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efﬁciency within

electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU),

Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our

comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines,

emphasizing development approaches and algorithms speciﬁcally designed for mobility energy efﬁciency.

The study shows the effect of various control units on energy efﬁciency by carefully examining case

studies and real-world applications. Employing a critical assessment, the paper examines the advantages

and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their

effectiveness in various situations. The presented study is typically related to the relationship between ECUs

and their sub-branches through an integrated approach. By comparing current control approaches, the study

offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall

control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of

EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating

knowledge of multiple control areas in EV. The research effort will give researchers a path forward and

make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area

of integrated ECUs in EVs.

INDEX TERMS Electronic Control Unit, Vehicle Control Unit, Electrical Power Steering, Electronic

Stability Control, Body Control Unit.

I. INTRODUCTION

In recent years, the global automotive industry has witnessed

a signiﬁcant shift towards electric vehicles (EVs) as a promis-

ing solution to mitigate environmental challenges associated

with traditional internal combustion engine (ICE) vehicles

[1], [2]. The need to reduce greenhouse gas emissions and

tackle climate change has propelled governments, organiza-

tions, and individuals to embrace cleaner and more sustain-

able transportation options. The transition to electric vehicles

offers a clear pathway to achieve these environmental goals,

primarily focusing on reducing carbon dioxide (CO2) emis-

sions, air pollutants, and dependence on fossil fuels [3].

Figure 1 shows the graph obtained using the data sourced

from the International Energy Agency’s Global EV Data

Explorer [4], which provides comprehensive information on

EV charging points, EV stock, EV sales, and other data of

different countries. For our analysis, we use stock and sales

data for battery electric vehicles (BEV) and plug-in hybrid

electric vehicles (PHEV) worldwide. In addition, it presents

the EV (BEV, PHEV) stocks and sales worldwide from 2020

in projections to 2030. We indicate the years 2020–2030

on the x-axis to provide the pattern of EV sales and stocks

VOLUME 11, 2023 1

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

chronologically. The sales data for BEVs and PHEVs are

displayed on the left y-axis, highlighting the rising demand

for electric cars worldwide. The stocks of BEVs and PHEVs,

representing the total number of these cars in the market, are

on the right y-axis. The graph shows that with the sales of

2 million BEVs and 970,000 PHEVs, the EV market started

slowly in 2020. Sales of BEVs and PHEVs saw notable

increases by 2022, hitting 7.3 million and 2.9 million units,

respectively. While PHEV stocks steadily increased to 7.9

million units, BEV stocks reached 18 million. 2025 is pre-

dicted to be a signiﬁcant turning point, with BEV sales rising

to 16 million units and exceeding PHEV sales of 4.5 million

units. In conclusion, the graph illustrates a steady growth

in EV adoption over the ﬁve years, with sales increasing

substantially yearly. The noteworthy ﬁnding is the increase

in BEV sales, which is more than PHEV sales. The graph

shows a sharp increase in BEV sales between 2025 and 2030,

pointing to a clear shift towards BEVs worldwide.

FIGURE 1. Overview of the EV statistics

The continuous advancements in electric vehicle technol-

ogy have further bolstered the case for their widespread adop-

tion and continued development. In [5], authors examined

how the market for EVs is changing, focusing on technol-

ogy developments and how they contribute to the goal of

a carbon-neutral society. With ongoing research and devel-

opment efforts, innovative solutions have emerged in areas

such as battery technology [6], charging infrastructure [7],

and control units, driving signiﬁcant improvements in EV

performance, range, and overall user experience. In [6], Abro

et al. explores the most recent advances in battery technology

developments for EVs, including a thorough summary of the

latest advances in energy management, battery design, and

optimization. In [7], Acharige et al. provide insights into the

changing environment of EV charging systems by consider-

ing dedicated converter topologies, control techniques, and

standard compliance to improve charging efﬁciency and grid

support. Future trends indicate integrating advanced control

units in electric vehicles, enabling enhanced Vehicle-to-Grid

(V2G) capabilities, intelligent energy management systems,

and seamless connectivity with smart grids [8]. These devel-

opments aim to optimize energy consumption [9], minimize

charging times [10], and maximize the utilization of renew-

able energy sources, thereby strengthening the environmental

beneﬁts of electric vehicles [11].

Electric vehicles (EVs) have witnessed signiﬁcant ad-

vancements in control technologies, contributing to im-

proved performance, efﬁciency, and overall driving experi-

ence. Modern EVs incorporate various control technologies

to manage multiple aspects of their operation [12]. These

include powertrain control systems, energy management sys-

tems, regenerative braking control, and vehicle stability con-

trol. Powertrain control systems optimize the coordination

between the electric motor, battery, and other components

to deliver efﬁcient and responsive power delivery [13]. En-

ergy management systems monitor and control the energy

ﬂow within the vehicle, ensuring optimal utilization of the

battery’s capacity [14]. Regenerative braking control tech-

nologies capture kinetic energy during braking, converting it

into electrical energy for recharging the battery [15]. Vehicle

stability control systems utilize sensors and actuators to en-

hance safety and stability during acceleration, deceleration,

and cornering [16].

Much research on EV powertrain control systems has been

done recently in the literature. The authors of [13] provide

a novel method for hybrid Deep Reinforcement Learning

(DRL) strategy-based powertrain control optimization in Hy-

brid Electric Vehicles (HEVs). Using preexisting vehicle data,

the authors pre-train ofﬂine neural networks (NNs) combined

with an online DRL method. This combination method effec-

tively achieves optimal power source management for HEVs

while speeding up the learning process and improving fuel

efﬁciency in simulated instances. In [14], authors addressed

energy management systems in EVs. They carried out an

extensive investigation that centered on the creation of an En-

ergy Management System (EMS) for a plug-in hybrid electric

vehicle (HEV) through the use of deep learning techniques,

particularly Recurrent Neural Networks (RNNs). Based on

Real Driving Emissions (RDE) compliant vehicle missions,

the AI models were trained ofﬂine to decrease carbon diox-

ide emissions. The integration of regenerative braking con-

trol technology has also gained attention in recent research.

With an emphasis on energy savings and increased driving

distance, [15] investigated the regenerative braking control

approach for EVs with four in-wheel motors. The approach

involves splittingthe braking force between the hydraulic and

motor braking systems and between the front and back axles.

The efﬁcacy of the suggested fuzzy control-based method,

which uses a Mamdani fuzzy controller, is conﬁrmed by sim-

ulation, indicating that it can improve regenerative braking

efﬁciency for EVs. Within vehicle stability control systems,

the study of [16] is noteworthy. Their study concentrated

on the advantages of using axle electric motors for vehicle

stability control in hybrid electric vehicles (HEVs). It presents

a combined electrohydraulic braking and axle motor torque

control technique for differential braking and driving stability

management. The devised technique is compared with current

2VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

vehicle stability control systems using real-time simulations

of intense steering maneuvers, assessing energy consumption,

performance, and stability characteristics for hybrid electric

sport utility vehicles. Our review study analyzes these dif-

ferent control technologies in a uniﬁed form under different

control units in EVs to evaluate their capabilities, beneﬁts,

and challenges, advancing EV performance, efﬁciency, and

sustainability while ensuring optimal energy management

and vehicle control.

The signiﬁcant contributions of our research study are

listed below:

•The study provides a detailed review of different de-

velopment strategies and control algorithms that can be

adapted to efﬁciently control different EV control units.

•The study highlights the conﬁguration and sensors uti-

lized in each control unit of EVs.

•Moreover, the comparison of different control algo-

rithms adopted to control EVs for mobility energy ef-

ﬁciency is presented.

•Finally, the current research challenges are highlighted

to pave the way for future research directions.

The proposed study is organized as shown in Figure 2.

After the introduction section, development strategies and al-

gorithms of integrated ECU for mobility energy efﬁciency are

presented in section II followed by the development strategies

and algorithms of VCU, EPS, ESC, and BCU in the next

sections III, IV, V, and VI. The comparison of integrated ECU

algorithms for mobility energy efﬁciency is summarized in

section VII. Section VIII highlights the shortcomings of avail-

able solutions and future directions. Finally, the conclusion is

presented in section IX.

A. THE RESEARCH METHODOLOGY FOR ECU

This section highlights a comprehensive overview of the

detailed methodology followed for the research. Electronic

Control Unit (ECU) and its various types are chosen as

research methods for determining electric vehicle control

strategies.

B. PLANNING THE REVIEW

Review planning involves key components such as formulat-

ing research questions, identifying relevant data sources, and

creating search strings.

C. RESEARCH QUESTIONS

The research questions addressed in this review are:

RQ1: What are the different types of electronic control units

used in electric vehicles?

RQ2: What are the emerging control techniques in electronic

control units for electric vehicles?

D. DATA SOURCES FOR LITERATURE STUDY

An iterative technique was utilized to comprehensively ex-

plore various scientiﬁc databases and digital libraries. The

selection of these libraries was based on current research

trends. Data sources included are:

FIGURE 2. Detailed flow of the proposed research study

•Google Scholar

•Science Direct

•Wiley Online Library

•IEEE Xplore Library

E. SEARCH STRINGS

In this research study, we derived keywords from the existing

literature that aligned with our research questions to construct

the search strings. Boolean operators "OR" and "AND" com-

bine these keywords effectively. The search strings used to

explore online databases were as follows: (“Control Technol-

ogy” OR “Control Method”) AND (“Electronic Control Unit”

OR “Vehicle Control Unit” OR “Electrical Power Steering”

OR “Body Control Unit”) AND (“Electric Vehicles” OR

“Hybrid Electric Vehicles” OR “Autonomous Vehicle”) AND

(“Energy Management” OR “Energy Efﬁciency”).

II. INTEGRATION OF VARIOUS CONTROL UNITS IN EV

FOR ENHANCING ENERGY EFFICIENCY

As Electric Vehicles (EVs) continue to gain popularity, the au-

tomotive industry is constantly evolving to meet the demands

of this emerging market. One crucial aspect of EV develop-

ment is integrating various control units to ensure the efﬁcient

and coordinated functioning of different vehicle systems. In

this context, the concept of an integrated Electronic Control

Unit (ECU) has emerged as a signiﬁcant advancement in EV

technology. An integrated ECU combines multiple control

VOLUME 11, 2023 3

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

units into one unit, streamlining communication and enhanc-

ing overall vehicle performance [17].

The integrated ECU in an electric vehicle typically incor-

porates four main control units: Vehicle Control Unit (VCU),

Electric Power Steering (EPS), Electronic Stability Control

(ESC), and Body Control Unit (BCU). All these control units

are crucial in ensuring the safety, efﬁciency, and smooth

operation of EVs [18].

Furthermore, the VCU acts as the central brain of the

integrated ECU system, managing and coordinating various

subsystems and components of the electric vehicle. It controls

the powertrain, battery management system, and other auxil-

iary systems. The VCU receives data from multiple sensors

and makes real-time decisions to optimize the vehicle’s per-

formance, including torque distribution, regenerativebraking,

and energy management [19].

The EPS control unit gives the driver precise and effortless

control over the vehicle’s steering. It uses sensors to measure

driver input and vehicle parameters, allowing the EPS system

to adjust the steering assistance accordingly. Integrating the

EPS control unit into the ECU allows the EV steering re-

sponse to coordinate seamlessly with other control functions,

enhancing overall vehicle stability and safety [20].

Similarly, the ESC control unit is designed to ensure vehi-

cle stability and prevent loss of control in critical situations. It

monitors various parameters, including steering angle, wheel

speed, and yaw rate. By applying individual wheel braking

and adjusting power distribution, the ESC control unit can

selectively intervene to correct understeer or oversteer con-

ditions. Integrating the ESC control unit with the integrated

ECU allows faster and more precise response times, enhanc-

ing vehicle safety during cornering and emergency maneuvers

[21].

Furthermore, the BCU controls various electrical and elec-

tronic systems within the vehicle’s body, including lighting,

climate control, door locks, windows, and other comfort and

convenience features. Integrating the Body Control Unit into

the integrated ECU allows the coordination and management

of these body-related functions to be streamlined, enhancing

overall vehicle functionality and user experience [22].

Moreover, Figure 3 shows the layout of ECU in EV. The

front axle is driven by a permanent magnet synchronous

motor connected to a transmission. The motor is regulated by

a Motor Control Unit (MCU) supervised by VCU. The Bat-

tery Management System (BMS) controls the battery pack,

supplying energy to move the vehicle and storing recovered

electric energy. Each wheel has a disc brake, monitored by

the Electronic Stability Control (ESC) system. Electric Power

Steering (EPS) assists in controlling the vehicle’s steering,

reducing energy consumption, and ensuring stability. Addi-

tionally, BCU manages various electronic systems in the ve-

hicle, including exterior and interior lighting and windshield

wipers. The ECUs coordinate the control of all these compo-

nents by communicating and exchanging their requests. [23].

FIGURE 3. Layout of Integrated ECU control in EV

A. ELECTRIC VEHICLE CONTROL CONCEPT

Electric vehicle control plays an important part in EVs’ efﬁ-

cient operation and optimization. As the world shifts towards

sustainable transportation, developing advanced control sys-

tems for EVs becomes increasingly important. These control

systems are responsible for managing and coordinating dif-

ferent units of EV, such as the powertrain, energy storage sys-

tem, and auxiliary systems. By intelligently controlling these

components by introducing integrated ECU control, EVs can

achieve optimal performance, maximize energy efﬁciency,

and enhance the overall driving experience [24].

Effective control of electric vehicles offers several sig-

niﬁcant advantages [25]. Firstly, it optimizes power distri-

bution and utilization within the vehicle, improving energy

efﬁciency. By effectively regulating the power transfer to the

motor using the battery, control systems can optimize en-

ergy usage, minimize losses, and extend the vehicle’s driving

range [26]. Moreover, efﬁcient control of EVs helps achieve

better overall performance, including acceleration, braking,

and handling. Moreover, ensuring effective control of EVs

is critical for upholding vehicle safety. Control systems are

responsible for monitoring and protecting the battery pack,

ensuring proper charging and discharging behavior, and pre-

venting hazardous conditions, such as overcharging or over-

heating [27]. Additionally, control algorithms play a crucial

role in integrating various safety features, such as regenerative

braking and stability control systems, in enhancing the overall

safety of the EV [28].

To enable effective control of electric vehicles, certain

requirements must be fulﬁlled. These include precise sensing

and measurement systems to provide accurate data on vehicle

parameters like speed, position, battery state of charge, and

temperature [29]. Additionally, sophisticated control algo-

rithms are needed to process this data and make real-time

decisions regarding power distribution, torque control, energy

management, and vehicle dynamics. These algorithms should

be capable of adapting to changing driving conditions, opti-

4VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

mizing power ﬂow, and ensuring the seamless integration of

different vehicle subsystems [30]. Moreover, electric vehicle

control requires a reliable and robust communication network

to facilitate the exchange of information between various

components, such as the battery management system, motor

controller, and vehicle control unit. This network enables co-

ordinated control actions and ensures the proper functioning

of the entire EV system [31].

B. ELECTRIC VEHICLE AND ITS TYPES

Much research is going on on electric vehicles, which promise

future solutions to environmental issues. The goals of these

research studies are the focus of many electric vehicle conﬁg-

urations and their control. Battery Electric Vehicles (BEVs)

run entirely on electricity and are powered by rechargeable

batteries. Hybrid Electric Vehicles (HEVs) combine an inter-

nal combustion engine with an electric motor and a battery;

Plug-in Hybrid Electric Vehicles (PHEVs) have both an inter-

nal combustion engine and a rechargeable battery, and Fuel

Cell Electric Vehicles (FCEVs) use a fuel cell to generate

electricity on board by combining hydrogen gas from a tank

with oxygen from the air are examples of EVs [23]. BEVs

often face challenges primarily related to their batteries, mak-

ing them more suitable for smaller electric vehicles with short

distances and lower speeds in local transportation. These ve-

hicles require smaller battery capacities. While cost remains

a signiﬁcant concern, HEVs can meet consumer demands and

offer additional beneﬁts. FCEVs have the potential to become

popular vehicles in the future. However, the technology is in

its early stages, with price and fueling infrastructure being

the main areas of concern [24]. The user’s daily commute,

availability of charging infrastructure, ﬁnancial restraints,

and environmental concerns are some variables that inﬂuence

the choice of EV. While some consumers choose PHEVs

because of their ﬂexibility and extended driving range, others

may emphasize lowering the environmental impact and go for

BEVs [8]. The EV types and their characteristics, along with

issues in each type and the example models, are explained in

Table 1.

C. EV CONFIGURATION AND SENSORS

Sensors play a vital role in electric vehicles (EVs) by provid-

ing crucial information to control units, enabling precise mon-

itoring and control of various subsystems [39]. These sensors

serve as the sensory organs of the EV, capturing real-time

data on key parameters such as speed, position, temperature,

battery state of charge, and environmental conditions. This in-

formation allows the control units to make informed decisions

and implement effective control strategies, enhancing electric

vehicles’ performance, efﬁciency, and safety [40].

The EV system can be categorized into three primary

blocks, shown in Figure 4. To perceive the surrounding en-

vironment, the EV is equipped with various sensors, which

are hardware devices responsible for collecting data. The

gathered sensor data is then processed in the optimal control

block, where different components work together to extract

meaningful information from the sensor inputs. The optimal

control block utilizes optimal control algorithms to generate

controlled commands for actuators. To ensure the following

path, the control module sends control commands to the

vehicle, thereby overseeing its movement and trajectory [41].

FIGURE 4. Block diagram of EV system based on installed sensing and

actuating devices

D. DEVELOPMENT STRATEGIES OF INTEGRATED ECU FOR

MOBILITY ENERGY EFFICIENCY

The development strategy for integrated ECU in an electric

vehicle involves ensuring the system is reliable, efﬁcient, and

safe. To achieve these goals, the ECU must be designed to

operate in various conditions and environments, including

extreme temperatures, high humidity, and high vibrations.

The ECU must also be designed to handle large amounts

of data quickly and efﬁciently while maintaining low power

consumption. Additionally, the ECU must be designed with

safety in mind since it controls critical systems in the vehicle.

This involves implementing fail-safe mechanisms, such as

redundant sensors and controllers, to ensure that the vehicle

can operate safely in case of a failure inside the system.

Overall, the design of an ECU for an electric vehicle requires

a balance of performance, efﬁciency, and safety [42].

Figure 5 shows the main development strategies of inte-

grated ECU to improve mobility energy efﬁciency.

Development strategies for integrated ECU include predic-

tive analytics, optimal operation based on machine learning,

thermal management, power electronics, and energy recuper-

ation. Predictive analytics can be used to analyze data from

sensors and other sources to help optimize the operation of

the ECU and the electric vehicle. Predictive analytics can

anticipate driving behavior [43], road conditions for route

optimization [44], and other factors that can affect energy

consumption and adjust power distribution and battery man-

agement accordingly. Machine Learning (ML) algorithms

can be used to optimize the operation of the ECU and the

VOLUME 11, 2023 5

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 1. Critical analysis of the characteristics of EV types

Ref. Electric Vehicle Type Advantages Disadvantages Example Models

[24],

[32]

Battery Electric Vehicle

(BEV) •No tailpipe emissions

•Lower operating costs

•Simple drivetrain with fewer mov-

ing parts

•Limited driving range per charge

•Long charging time

•Insufﬁcient charging infrastruc-

ture

•High initial cost of batteries

Tesla Model S, Nissan

Leaf, Chevrolet Bolt

[24],

[33],

[34]

Hybrid Electric Vehicle

(HEV) •Improved fuel efﬁciency

•Regenerative braking reduces en-

ergy wastage

•No need for external charging

•Complex powertrain systems

•Battery degradation over time

•Reliance on fossil fuels for the in-

ternal combustion engine

Toyota Prius, Honda Ac-

cord Hybrid, Ford Fusion

Hybrid

[35],

[36]

Plug-in Hybrid Electric

Vehicle (PHEV) •All-electric driving range for short

trips

•Flexibility for longer trips with the

internal combustion engine

•Regenerative braking and reduced

emissions

•Additional weight and complexity

from larger batteries

•Charging infrastructure availabil-

ity

•Balancing power demands be-

tween electric and internal com-

bustion engines

Chevrolet Volt, Mitsubishi

Outlander PHEV, BMW i3

REx

[24],

[37],

[38]

Fuel Cell Electric Vehi-

cle (FCEV) •Longer driving range compared to

BEVs

•Fast refueling time

•Zero tailpipe emissions

•Limited hydrogen fueling infras-

tructure

•High cost and limited availability

of fuel cell technology

•Hydrogen production and storage

challenges

Toyota Mirai, Hyundai

Nexo, Honda Clarity Fuel

Cell

FIGURE 5. Taxonomy of the development strategies for integrated ECU

electric vehicle. ML can identify patterns in driving behav-

ior and road conditions and adjust the power distribution

and battery management accordingly. This can help to im-

prove energy efﬁciency and reduce carbon emissions [45].

Efﬁcient thermal management of the ECU can help to re-

duce power consumption and improve energy efﬁciency. This

can be achieved using advanced cooling systems and heat-

dissipation materials [46]. Advanced power electronics, such

as wide-bandgap semiconductors, can improve the efﬁciency

of the ECU and the electric vehicle. Wide-bandgap semicon-

ductors have lower resistance and switching losses, which

results in less heat generation and higher efﬁciency [47].

Energy Recuperation involves using energy recuperation sys-

tems, such as regenerative braking, which can help capture

the energy that would otherwise be lost during braking and

recharge the battery. This can improve energy efﬁciency and

reduce the need for external charging [48].

Table 2 shows current developments in the ﬁeld of EV

technology, with an emphasis on development techniques

for integrated ECU. The table highlights several noteworthy

research papers demonstrating innovative methods to im-

prove EV performance, energy efﬁciency, and sustainability.

The most common development strategies include machine

learning algorithms to analyze data from the ECU and pre-

dict driving behavior. This information can then be used to

optimize the performance of the EV and improve energy

efﬁciency [49]. Integrating real-world data combines data

from the ECU with real-world data, such as driving conditions

and weather information, to develop more accurate and robust

6VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

control models [50]. Advanced electronics technologies, such

as ﬁeld-programmable gate arrays (FPGAs), are used to de-

velop more efﬁcient and powerful ECUs [51].

Table 2 offers a comprehensive overview of the latest

research on development strategies of Integrated ECU in

EVs. Bangroo et al.’s approach [52] utilizing AI-based pre-

dictive analytics stands out for its potential to enhance

decision-making, energy efﬁciency, and cybersecurity in

electric/hybrid vehicles. However, using large datasets raises

concerns about data privacy that need to be addressed for

practical implementation. Canal et al.’s study (Ref. [53])

integrating real ECU data with machine learning algo-

rithms demonstrates impressive accuracy in identifying driv-

ing patterns, emphasizing the real-world applicability of their

methodology. Nevertheless, concerns about overﬁtting and

the need for robust regularization techniques are vital aspects

to consider in further applications. Broatch et al.’s research

(Ref. [54]) on integrated thermal management systems stands

out for its incorporation of extensive real-world data, enhanc-

ing the accuracy of simulation results. However, expanding

the scope to other vehicle platforms like plug-in hybrids and

fully electric cars could offer more comprehensive insights.

Gong et al.’s emphasis (Ref. [55]) on modern power electron-

ics technologies in EV powertrains paves the way for future

innovations. Yet, the lack of quantitative analysis regarding

energy efﬁciency remains a notable limitation. Chengqun

et al.’s innovative regenerative braking system (Ref. [56])

stands out for its efﬁcient energy recovery, especially for

aggressive driving styles, but could beneﬁt from adaptability

enhancements considering diverse factors affecting regenera-

tive braking efﬁciency.

Each development strategy has advantages and disadvan-

tages, as mentioned in table 2. For example, Al-based pre-

dictive analytics can effectively improve energy efﬁciency,

but it requires large amounts of data and can be complex to

implement [52]. Integration of real-world data can improve

the accuracy of control models, but it can be challenging to

collect and process large amounts of real-world data [57].

Advanced electronics technology can lead to more efﬁcient

and powerful ECUs, but it can be expensive to develop and

implement [51]. Integrated ECUs can potentially improve

EVs’ performance, efﬁciency, and safety. Still, they are facing

a few challenges mentioned in this section, which can be ad-

dressed for future research to improve EV energy efﬁciency.

E. CONTROL ALGORITHMS OF INTEGRATED ECU FOR

MOBILITY ENERGY EFFICIENCY

In an EV, the integrated ECU refers to the central electronic

control unit responsible for managing and coordinating var-

ious electrical systems and components within the vehicle.

It serves as the "brain" of the EV, overseeing the operation

of critical functions and ensuring their proper integration.

An ECU is an embedded system in EVs that controls one

or multiple systems or subsystems within a vehicle. In mod-

ern vehicles, numerous ECUs are utilized, including but not

limited to the powertrain control unit (PCU), transmission

control unit (TCU), engine control unit (ECU), brake control

module (BCM), suspension control unit (SCU), and body

control unit (BCU). [58].

Furthermore, Figure 6 shows the sub-division of ECU,

which is required to be controlled in EV for energy manage-

ment.

FIGURE 6. Taxonomy of the control algorithms of integrated ECU

III. DEVELOPMENT STRATEGIES AND ALGORITHMS OF

VCU FOR MOBILITY ENERGY EFFICIENCY

A. VCU CONFIGURATION AND SENSORS

In an autonomous vehicle, the VCU has a critical role in con-

trolling various aspects of the vehicle’s operation. To achieve

this, the VCU relies on a range of sensors, enabling it to make

informed decisions and optimize the vehicle’s performance.

Several key sensor types used in the VCU of autonomous

vehicles include powertrain control sensors, battery manage-

ment system sensors, regenerative braking control sensors,

and thermal management system sensors [59], as shown in

Figure 7.

Powertrain control sensors, such as the throttle position

sensor, measure the position of the accelerator pedal and

provide input to the VCU regarding the desired power output

from the electric motor. These sensors allow the VCU to

regulate the powertrain system and optimize its efﬁciency and

performance [60].

Furthermore, Battery management system sensors are cru-

cial for monitoring the battery pack’s state. They provide

data on parameters like the State of Health (SoH), temper-

ature, and State of Charge (SoC). By analyzing this data, the

VCU can ensure optimal battery performance, manage energy

consumption, and protect the battery from overcharging or

overheating [61].

In addition, Regenerative braking control sensors, includ-

ing wheel speed sensors and brake pressure sensors, enable

the VCU to engage regenerative braking, which converts the

kinetic energy into electrical energy for energy storage in

the battery. These sensors provide critical input for the VCU

to adjust the regenerative braking force and optimize energy

recovery. Thermal management system sensors monitor the

temperature of various components in the vehicle, such as

the battery pack, motor, and power electronics. These sensors

provide feedback to the VCU, enabling it to implement appro-

VOLUME 11, 2023 7

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 2. Latest Research on Development Strategies of Integrated ECU in EV

Ref. Development

Strategy

Description Merits Demerits

[52]

AI-based predictive

analytic approach

Use of AI technology to en-

hance decision-making and en-

ergy efﬁciency in integrated

control of electric/hybrid vehi-

cles while also addressing cy-

bersecurity vulnerabilities

Sustainability: Encourages the usage of

electric/hybrid vehicles to mitigate climate

change and promotes sustainable transporta-

tion

Efﬁciency: AI-based predictive analysis can

improve the energy efﬁciency, emissions re-

duction, and general sustainability of elec-

tric/hybrid vehicles

Security: Use of AI technology to address

cybersecurity ﬂaws in electric/hybrid vehi-

cles

Data Privacy: Large data sets fre-

quently used in predictive analytics must

be protective to mitigate privacy issues

and legal consequences.

[53]

Machine Learning Al-

gorithms:

K-Means,

Logistic Regression,

XG-Boost

Combined real data from

the ECU of an EV with

machine learning algorithms

to assess driving patterns,

enhance safety, and save fuel

consumption.

Efﬁciency Improvement: Attaining 100%

accuracy, precision, and recall over several

algorithms indicates the resilience and efﬁ-

cacy of the suggested methodology in pre-

cisely identifying driving behavior.

Real-World Applicability: By utilizing ac-

tual data from a test car, the results are more

applicable to real-world driving situations

and are, therefore, more useful and relevant.

Over-ﬁtting Risk: Over-ﬁtting preven-

tion strategies may be the problem

while implementing such ML tech-

niques. Inadequate regularization tech-

niques might cause machine learning

models to learn the training data by heart

rather than the underlying patterns, re-

sulting in poor generalization on unob-

served data.

[54]

Integrated thermal

management system

In-depth experimental

measurements are carried out

to investigate the integration

of thermal ﬂows. Two heat

management systems were

tested at different temperatures

and real driving emission

cycles.

Integration of Real-World Data: To ensure

accurate model development and validation,

the study incorporates a large amount of ex-

perimental data from an Internal Combus-

tion Engine (ICE). Incorporating real-world

data improves the simulation results’ accu-

racy and dependability.

Novel Co-Simulation Technique: Using

Functional Mock-up Interface (FMI), the re-

search presents an innovative approach to co-

simulation that integrates high-ﬁdelity ICE,

thermohydraulic, and battery models.

Limited Relevance Scope: Subsequent

investigations may examine the cus-

tomization of integrated thermal man-

agement systems for other platforms,

such as plug-in hybrids and completely

electric cars, to promote more extensive

progress in thermal management tech-

niques.

[55]

Advanced Power

Electronics

Technology

Analysis of Controller

Hardware-in-the-Loop

(CHIL) simulations in EV

powertrains with a focus on

power electronics technologies

Emphasis on modern Technology: To en-

sure simulation accuracy, emphasis was

placed on modern power electronics tech-

nologies, such as ﬁeld-programmable gate

arrays (FPGAs).

Prospects for the Future: EV powertrain

control innovation is being encouraged by

presenting future directions, including model

reﬁnement and integration with Digital Twin

conﬁgurations.

Energy Efﬁciency Evaluation: This

study does not analyze The use of mod-

ern power electronics devices in electric

vehicle powertrains to boost energy efﬁ-

ciency.

[56]

Regenerative braking-

based energy recov-

ery management con-

trol strategy

An innovative EV regenerative

braking system maximizes en-

ergy recovery by utilizing a

driver model to predict driving

behavior.

Efﬁcient Energy Optimization: Improved

energy recovery efﬁciency, especially for ag-

gressive drivers using advanced IDP (Itera-

tive dynamic programming)-BLSTM (Bidi-

rectional Long Short Term Memory) control

approach

Limited Adaptability to Diverse Fac-

tors: Various factors (battery state, tem-

perature, and brake system health) that

affect the efﬁciency of the regenerative

braking system can also be included

priate thermal management strategies, including cooling or

heating, to maintain optimal operating temperatures and pre-

vent overheating [62]. All these sensors are controlled by the

VCU and coordinated by the Electronic Control Unit (ECU),

which serves as the central control hub. The ECU processes

data from these sensors and inputs from other subsystems to

make decisions and send control commands to various vehicle

components.

B. DEVELOPMENT STRATEGIES OF VCU FOR MOBILITY

ENERGY EFFICIENCY

The development strategies of VCU focus on improving the

vehicle’s performance while reducing energy consumption.

FIGURE 7. Sensors for vehicle control unit

8VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This is achieved through advanced strategies that optimize

the vehicle’s systems operation. For example, the VCU can

be designed to optimize the regenerative braking system to

recover energy during braking [63].

FIGURE 8. Taxonomy of the development strategies for VCU

Development Strategies for VCU to improve mobility en-

ergy efﬁciency are illustrated in Figure 8. Integrating ad-

vanced sensors is crucial to enhance the capabilities of

the VCU. These sensors include position, wheel speed, ac-

celerometer, gyroscopes, and other environmental and vehicle

condition sensors. The VCU relies on accurate and real-time

data from these sensors to make informed decisions regarding

powertrain control, energy management, and vehicle stability

[64]. Efﬁcient powertrain control is also required to maximize

the performance and range of EVs. The VCU should optimize

powertrain components such as the electric motor, battery,

inverter, and transmission. Development strategies involve

ﬁne-tuning control algorithms to manage torque distribu-

tion, regenerative braking, energy recuperation, and thermal

management. This ensures smooth power delivery, improved

energy efﬁciency, and optimal utilization of the electric pow-

ertrain system [65]. Effective energy management is critical

for electric vehicles. The VCU should incorporate intelligent

algorithms that monitor the state of charge, battery temper-

ature, and power demand. By dynamically adjusting power

distribution, the VCU can optimize energy usage and balance

the power demands of various vehicle systems.

Additionally, the VCU can facilitate features like predictive

range estimation, route planning, and energy-saving modes

to increase the overall efﬁciency of EVs [66]. The VCU con-

tributes to ensuring the safety and stability of EVs. Develop-

ment strategies involve integrating advanced stability control

systems, like Electronic Stability Control (ESC), traction con-

trol, anti-lock braking systems (ABS), and torque vectoring.

The VCU should employ sophisticated control algorithms

that continuously monitor vehicle dynamics and make neces-

sary adjustments to prevent loss of control, improve stability,

and enhance overall driving safety [67].

Table 3 thoroughly reviews current research on VCU’s

development strategies in EVs. These strategies include many

innovative techniques and deal with multiple aspects of

VCU development. An EV transportation system that uses

blockchain and machine learning technologies to improve

data security is presented in the research by [68]. However,

there is room for future development in ﬁne-tuning the system

for better response time and overall performance. A two-

step technique for accurate EV powertrain optimization is

presented in [65], greatly reducing computation time. The

article may, however, beneﬁt from emphasizing the energy

efﬁciency gains made possible by well-optimized engine

components. Additionally, a new co-simulation method is

demonstrated by the study presented in [69], which carries

out extensive experimental measurements to explore the in-

tegration of thermal ﬂuxes. By developing integrated thermal

management systems speciﬁcally for different car platforms,

the research’s applicability might be increased, and a more

thorough comprehension of thermal management strategies

could be encouraged. Lastly, the examination presented in

[70] concentrates on EV powertrain power electronics tech-

nology. The inﬂuence of power electronics devices on energy

efﬁciency in EV powertrains is not quantitatively evaluated

in the study, although it highlights current technology and

opportunities for future research paths. These critical assess-

ments underscore opportunities for reﬁnement and further

exploration of VCU strategies in EVs.

C. VCU ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY

The VCU is a control unit that controls various functions of

EVs, including steering, acceleration, and braking control.

The VCU is designed to optimize the performance of EVs

while also ensuring the safety of the passengers [71]. The

VCU can be designed to control the operation of various

powertrain components (engine, transmission, electric motor)

to reduce energy consumption. This can be achieved through

optimal gear selection, intelligent engine starts/stop systems,

and regenerative braking [23]. The VCU can be designed to

control the battery pack that powers the vehicle. It manages

battery charging and discharging and monitors its temperature

and state of charge (SoC) while ensuring battery safety and

efﬁciency [43]. VCU can be designed to capture and store

energy that would otherwise be wasted (e.g., through regen-

erative braking or exhaust gas heat recovery) and use it to

power auxiliary systems, reducing the load on the primary

power source [72]. VCU can also be designed to ensure the

primary goal of EV thermal management is to control the

temperatures of the battery, motor, and crew cabin to ensure

that all are working at optimal temperatures. The vehicle’s

capacity for heat dissipation is coordinated and controlled

by a thermal management system [73]. Table 4 highlights

the control algorithms, strength, and limitation of VCU for

VOLUME 11, 2023 9

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 3. Latest Research on Development Strategies of VCU in EV

Ref. Development

Strategy

Description Merits Demerits

[68]

Sensor data collection

and secure processing

Developed an effective EV

transit system by utilizing

blockchain and machine

learning technology

Data Security: Utilized cutting-edge tech-

nology (Blockchain, Machine Learning) to

improve security and privacy while exchang-

ing data about EVs.

Response Time Improvement: Reﬁne-

ment and optimization are needed to re-

duce latency and improve overall system

performance.

[65]

Precise and effective

EV powertrain opti-

mization

Developed a two-step analyti-

cal methodology for optimizing

EV powertrains. The ﬁrst stage

examined motor features and

efﬁciency mapping, while the

second stage used motor data to

assess vehicle performance.

Computational Efﬁciency: Considerable

reduction in calculation time (approximately

250 times quicker) by the use of surrogate

models in multi-objective optimization

Energy Efﬁciency Improvement: Op-

timized powertrain components result

in noticeable increases in energy efﬁ-

ciency, which is not highlighted in the

paper and can be explored in the future.

[69]

Energy management In-depth experimental

measurements are carried out

to investigate the integration

of thermal ﬂows. Two heat

management systems were

tested at different temperatures

and real driving emission

cycles.

Integration of Real-World Data: To ensure

accurate model development and validation,

the study incorporates a large amount of ex-

perimental data from an Internal Combus-

tion Engine (ICE). Incorporating real-world

data improves the simulation results’ accu-

racy and dependability.

Novel Co-Simulation Technique: Using

Functional Mock-up Interface (FMI), the re-

search presents an innovative approach to co-

simulation that integrates high-ﬁdelity ICE,

thermohydraulic, and battery models.

Limited Relevance Scope: Subsequent

investigations may examine the cus-

tomization of integrated thermal man-

agement systems for other platforms,

such as plug-in hybrids and completely

electric cars, to promote more extensive

progress in thermal management tech-

niques.

[70]

Stability Analysis of Controller

Hardware-in-the-Loop

(CHIL) simulations in EV

powertrains with a focus on

power electronics technologies

Emphasis on modern Technology: To en-

sure simulation accuracy, emphasis was

placed on modern power electronics tech-

nologies, such as ﬁeld-programmable gate

arrays (FPGAs).

Prospects for the Future: EV powertrain

control innovation is being encouraged by

presenting future directions, including model

reﬁnement and integration with Digital Twin

conﬁgurations.

Energy Efﬁciency Evaluation: This

study does not analyze The use of mod-

ern power electronics devices in electric

vehicle powertrains to boost energy efﬁ-

ciency.

mobility energy efﬁciency in EVs.

IV. DEVELOPMENT STRATEGIES AND ALGORITHMS OF

EPS FOR MOBILITY ENERGY EFFICIENCY

A. EPS CONFIGURATION AND SENSORS

In EVs, precise and reliable steering control is paramount for

safe and accurate navigation. Steering control sensors are cru-

cial in providing essential input to the autonomous vehicle’s

control system [84]. Two key sensors involved in steering

control are the torque sensor and steering angle sensor, which

work in conjunction with the Electrical Power Steering (EPS)

system, ultimately controlled by the Electronic Control Unit

(ECU) as illustrated in Figure 9.

The steering angle sensor measures the rotational position

of the steering wheel, providing real-time information on

the driver’s intended direction. It enables the autonomous

vehicle’s control system to accurately interpret the driver’s

steering inputs and translate them into appropriate commands

for the steering actuator [85]. On the other hand, the torque

sensor measures the level of torque applied to the steering

column. It detects the driver’s effort to turn the steering wheel,

providing critical feedback on the driver’s intentions and the

resistance encountered while maneuvering the vehicle. The

control system utilizes This torque information to enhance

steering control precision and implement advanced driver as-

sistance features, such as torque-based steering interventions

and lane-keeping assistance [86].

Furthermore, the steering angle and torque sensors are in-

tegrated into the EPS system, replacing traditional hydraulic

power steering with an electrically assisted mechanism. The

EPS system utilizes electrical power, controlled by the ECU,

to assist the driver’s steering inputs and deliver the appropriate

steering response. The ECU processes the steering angle and

torque sensor data, applying sophisticated control algorithms

to regulate the EPS system’s behavior and provide the desired

steering assistance [87]. Integrating these sensors and the EPS

system into the overall control architecture of autonomous

vehicles enables precise and dynamic steering control, allow-

ing for smooth maneuvering and accurate trajectory tracking.

The ECU, the central control unit, coordinates the interactions

between the sensors, EPS system, and other control modules

to achieve safe and reliable autonomous steering [88].

B. DEVELOPMENT STRATEGIES OF EPS FOR MOBILITY

ENERGY EFFICIENCY

Figure 10 illustrates the main development strategies for

EPS to improve mobility energy efﬁciency. The develop-

ment strategies for EPS in electric vehicles for mobility

energy efﬁciency revolve around efﬁcient power assistance

control, regenerative energy harvesting, intelligent steering

10 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 4. Control Algorithms of vehicle control unit in electric vehicles

Ref. Control Algorithm Description Strength Limitation

[74],

[75]

Proportional-

Integral-Derivative

(PID) Control

PID control is a commonly used algorithm that adjusts

the control signal based on the error between the de-

sired and actual states of the vehicle, helping maintain

stability and accuracy.

Easy Implementation, low

Computational cost, work well

for linear systems

Parameter tuning is required

for optimal performance, lim-

ited adaption for systems with

varying dynamics

[76],

[77]

Model Predictive

Control (MPC)

MPC is an advanced control algorithm that uses a

dynamic system model to optimize control inputs. It

considers predictions of future states and constraints

to determine the optimal control action.

Optimal control based on fu-

ture prediction, handle multiple

constraints at a time

High computational cost, sensi-

tive to model inaccuracies

[77],

[78]

Field-Oriented Con-

trol (FOC)

FOC is a motor control algorithm that enables precise

control of torque and ﬂux in an electric motor. It

decouples the torque and ﬂux components, allowing

independent control and efﬁcient utilization of the

motor’s capabilities

Accurate torque and speed con-

trol. It enhances motor per-

formance by reducing motor

losses

Accurate motor parameters set-

ting required, vulnerable to

varying parameters and noise

from sensor.

[79],

[80],

[81]

Direct Torque Con-

trol (DTC)

TDTC is a control algorithm that directly regulates

torque and ﬂux in an electric motor without calcu-

lating or controlling the motor currents. It offers fast

torque response and precise control by selecting the

optimal voltage vectors

Simple implementation com-

pared to FOC, fast torque re-

sponse with fewer ripples

Switching frequency is

increased, sensitivity to

parameter variations.

[82],

[83]

Adaptive Control Adaptive control algorithms adjust the control param-

eters based on real-time changes in system dynamics.

These algorithms continuously estimate and update

the model parameters to accommodate variations in

vehicle and environmental conditions

Robust control in varying op-

erating conditions, adaptable to

different vehicles and driving

scenarios

Precise parameter tuning is re-

quired, limitations for highly

non-linear systems.

FIGURE 9. Overview model of the steering control based on different

installed sensors

assistance, variable assistance levels, vehicle dynamics con-

trol systems integration, lightweight component design, and

advanced sensor integration. Implementing these strategies

can optimize the EPS system’s performance, minimize energy

consumption, and contribute to the overall energy efﬁciency

of electric vehicles.

Furthermore, EPS systems are more energy-efﬁcient than

traditional hydraulic power steering systems. The electric

motor in an EPS system only consumes power when steering

assistance is required, whereas hydraulic systems consume

power continuously [89]. High-efﬁciency electric motors can

be used in EPS systems to reduce energy consumption. These

motors have higher efﬁciency levels, resulting in lower en-

ergy losses during operation [90]. Another effective strategy

for enhancing mobility energy efﬁciency is the implementa-

tion of regenerative energy harvesting in EPS systems. By

utilizing regenerative braking technology, the EPS system can

FIGURE 10. Taxonomy of the development strategies for EPS

recover energy during braking or deceleration and convert

it into electrical energy that can be stored in the vehicle’s

battery. This harvested energy can then be utilized to power

other vehicle systems, reducing the overall energy consump-

tion of the electric vehicle [91]. Integrating intelligent steer-

ing assistance capabilities in EPS systems can signiﬁcantly

improve mobility energy efﬁciency. This involves developing

algorithms that adaptively adjust the steering assistance based

on various factors, such as road conditions, vehicle speed,

and driver behavior. The EPS system can minimize energy

VOLUME 11, 2023 11

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

consumption and optimize steering control by providing the

right assistance at the right time, resulting in improved energy

efﬁciency [92]. Efforts to enhance mobility energy efﬁciency

in electric vehicles should consider lightweight component

design for EPS systems. Lightweight materials and efﬁcient

design can help reduce the overall mass of the EPS compo-

nents, thereby reducing energy requirements for operation.

Optimizing the mechanical design, such as reducing friction

and improving bearing efﬁciency, can further contribute to

improved energy efﬁciency [93].

An overview of current research on EPS system devel-

opment strategies in EVs is given in Table 5. Although the

table provides insightful information on novel strategies for

improving torque output and energy efﬁciency in EPS, a few

important points need to be addressed. Firstly, questions con-

cerning the Differential Drive Collaborative Steering (DDCS)

control system’s adaptability to a range of working situations

and vehicle characteristics are raised by the system’s lack of

real-time optimization and performance assessment metrics

[94]. Secondly, the study concentrating on torque produc-

tion enhancement via ﬂux-switching Permanent Magnet Ma-

chines (FSPMMs) shows encouraging results; however, the

absence of a thorough assessment regarding its improvement

in energy efﬁciency leaves room for additional research on

this topic [95]. Furthermore, examining the energy efﬁciency

of electric SUVs in real-world driving situations gets recogni-

tion for its practical methodology, even though the complexity

brought about by the variety of real-world driving scenarios

may make energy efﬁciency assessments more difﬁcult [96].

Finally, a promising technological advancement is presented

by the creative application of a triboelectric nanogenerator

(TENG)-based sensor for driver intention detection in EPS

[97]. However, consistent performance of the sensor requires

addressing its sensitivity to environmental factors. These

important elements draw attention to areas that need more

research and improvement in EPS strategies for EVs.

C. EPS ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY

EPS is a system that uses an electric motor to assist in steering

a vehicle. EPS is designed to improve the efﬁciency and

performance of the vehicle’s steering system while reducing

its energy consumption. EPS focuses on reducing the energy

consumption of the vehicle’s steering system while improving

its performance. This is achieved using advanced control

algorithms that optimize the electric motor operation [98].

For example, the EPS system can be designed to adjust the

electric motor’s level of assistance based on the vehicle’s

speed and steering angle. The EPS system can adapt the

necessary steering effort required by the driver for turning

the steering wheel. Making it easier for the driver to steer the

vehicle [99]. The EPS system can adjust the responsiveness

of the steering, making it more or less sensitive to driver

input [100]. The EPS system can adjust the steering assist

to help stabilize the vehicle in different driving conditions,

such as during high-speed driving or in crosswinds [101]. The

EPS system can also adjust the feedback the driver receives

from the steering, providing a more or less connected feeling

between the driver and the road [102]. Table 6 summarizes

the EPS control algorithms for mobility energy efﬁciency in

EVs, including strength and limitation.

V. DEVELOPMENT STRATEGIES AND ALGORITHMS OF

ESC FOR MOBILITY ENERGY EFFICIENCY

A. ESC CONFIGURATION AND SENSORS

Electronic Stability Control (ESC) systems in electric ve-

hicles rely on sensors to accurately determine the driver’s

intentions and monitor the vehicle’s response. These sensors

play a crucial role in ensuring the effectiveness of the ESC

system. One key aspect is using speed and steering angle

measurements to ascertain the driver’s desired heading [110].

Concurrently, onboard silicon-based sensors, manufactured

using micromachining technologies, measure the vehicle’s

lateral acceleration and yaw rate. Over the past decade,

these Micro-Electro-Mechanical Systems (MEMS) sensors

have revolutionized ESC systems by signiﬁcantly enhancing

their size, cost, and reliability, surpassing traditional high-

precision mechanical sensors [111].

Furthermore, the ESC system’s control algorithm, residing

in the ESC controller, continuously receives data from the

wheel speed sensors, steering angle sensor, and yaw rate

sensor, as illustrated in Figure 11. The controller stores an

application algorithm that incorporates equations represent-

ing the vehicle’s dynamics [112]. By comparing driver inputs

to the vehicle’s response, the control algorithm determines

the need for intervention, such as applying the brakes through

hydraulic modulator valves or adjusting the throttle. When the

vehicle’s yaw rate aligns with lateral acceleration and speed,

it indicates a balanced response to the steering input [113].

In addition, the ESC controller can exchange data with

other controllers in connection with the vehicle network (con-

troller area network). This capability enables further enhance-

ments to the stability and control of EV [61]. Additionally,

ESC systems typically offer an option to disable the function,

which proves beneﬁcial in speciﬁc off-road driving condi-

tions or when using a smaller spare tire that may interfere

with the sensors. Some vehicle manufacturers even provide

an additional mode that allows drivers to explore the limits

of tire grip with reduced electronic intervention. However,

the ESC system automatically returns to normal operation

upon ignition restart, ensuring continued safety and stability

control [114].

B. DEVELOPMENT STRATEGIES OF ESC FOR MOBILITY

ENERGY EFFICIENCY

The development strategies for ESC in electric vehicles are

similar to those in traditional combustion engine vehicles.

The system typically uses sensors to monitor various vehicle

parameters, including speed, acceleration, steering angle, and

wheel rotation. Using this information, the ESC control unit

can detect when a loss of traction is about to happen and

intervene by reducing engine power or applying individual

brakes to speciﬁc wheels. However, in an electric vehicle,

12 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 5. Latest Research on Development Strategies of EPS in EV

Ref. Development

Strategy

Description Merits Demerits

[94]

Differential Drive

Collaborative

Steering (DDCS)

control system

Designed a DDCS optimal con-

trol approach with an energy-

saving mechanism that priori-

tizes reducing steering energy

usage.

Energy Efﬁciency Enhancement: The in-

novative DDCS technology offers an excep-

tional way to save energy in EVs with artic-

ulated steering.

Real-Time Optimization: Absence of

real-time optimization for performance

assessment under diverse working con-

ditions and vehicle parameters.

[95]

Enhancement of

torque production

by FSPMMs (Flux-

switching permanent

magnet machines) in

EPS system

Proposed a strategy to improve

torque generation that neutral-

izes negative torque contribu-

tions by inserting harmonic

currents.

Enhanced Torque Output: By optimizing

harmonic current parameters, a 9% increase

in mean torque was obtained.

Energy Efﬁciency Evaluation: Lack of

conclusive energy efﬁciency improve-

ment opens room for future research.

[96]

Electric SUV’s (Sport

Utility Vehicle)

energy efﬁciency

under real-world

driving conditions

Determined the effect of driv-

ing speed and the phases of re-

generative braking on energy

consumption

Real-World Analysis: Analyzing energy ef-

ﬁciency under various driving scenarios of-

fers insights into actual performance.

Complex Real-World Impact:Energy

efﬁciency evaluation is complicated by

real-world driving conditions

[97]

Intelligent steering

wheel utilizing

a triboelectric

nanogenerator

(TENG)-based sensor

Developed a TENG-based sen-

sor incorporated into the steer-

ing wheel to determine the

driver’s intentions.

Innovative Sensor Technology: Proposed a

new TENG-based sensor with quicker re-

sponse time and efﬁcient driver intention de-

tection

Sensitivity to Environmental Factors:

Environmental factors may impact the

sensor’s efﬁcacy in different situations.

TABLE 6. Control Algorithms of Electric Power Steering in Electric Vehicles

Ref. Control Algorithm Description Strength Limitation

[103],

[88]

Torque Overlay

Control

Torque overlay control is a basic EPS algorithm that

adds an electric motor-generated torque to the driver’s

steering input to assist the driver in steering maneuvers

and provides adjustable steering effort

Improved driver comfort by

providing adjustable steering

effort, easy implementation and

cost-effective

Limited usefulness in com-

plex driving situations, depends

upon inputs from driver for pre-

cise control

[104],

[98]

Active Front Steer-

ing

AFS is a control algorithm that modulates the steering

angle of the front wheels in response to various vehicle

and sensor inputs by adjusting the front wheel steering

angle

Enhances maneuverability dur-

ing high-speed or emergency

maneuvers

Certain limitations persist in

extreme driving scenarios, in-

crease complexity of steering

system.

[105],

[106]

Active Return Con-

trol

Active return control is an EPS algorithm that auto-

matically assists the driver in returning the steering

wheel to its neutral position after completing a turn

Improves vehicle stability, pro-

vides smoother steering inputs

Highly reliable response from

actuators, more complex EPS

system with higher cost.

[107],

[108]

Variable Gear Ratio

Control

Variable gear ratio control adjusts the steering gear

ratio based on various parameters, such as vehicle

speed, steering angle, and driving conditions

Improves vehicle stability at

high speeds, optimizes steering

characteristics

Requires additional mechanical

components, increases

complexity and cost

[109] Active Damping

Control

Active damping control is an advanced EPS algorithm

that adjusts the damping characteristics of the steering

system in real-time

Improves vehicle stability with

ride comfort, efﬁciently adjust-

ing damping force based on

changing road conditions

Highly reliable on actuators

and sensors, increases system

complexity and cost

FIGURE 11. Overview of the stability control using different installed

sensors [115]

there may be some differences in the design strategy for

ESC. For example, because electric motors can deliver instant

torque, ESC in an EV may need to respond even more quickly

than in a traditional vehicle. Additionally, some EVs may

use regenerative braking, which can complicate the control

strategy for ESC, as the system needs to balance the regen-

erative braking force with the conventional friction brakes to

maintain stability [116].

Furthermore, Figure 12 illustrates the main development

strategies for ESC to improve mobility energy efﬁciency.

ESC can be designed to encourage smoother driving by

reducing unnecessary acceleration and deceleration. By min-

imizing sudden speed changes, the vehicle can maintain a

more consistent energy output, which can help improve ef-

ﬁciency and extend the battery’s range [117]. The vehicle’s

design, including the placement of the battery and electric

VOLUME 11, 2023 13

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

FIGURE 12. Taxonomy of the development strategies for ESC

motor, can affect the stability of the vehicle and its energy

efﬁciency. By optimizing the weight distribution, ESC can

help ensure that the vehicle stays stable and balanced, reduc-

ing the need for corrective maneuvers that can waste energy

[118]. ESC can be designed to handle sensor fusion, which

involves combining data from multiple sensors such as LI-

DAR (light detection and ranging), RADAR (Radio detection

and ranging), wheel speed sensors, steering angle sensors,

and gyroscope, etc., to integrate data for real-time monitoring

resulting in enhanced accuracy [119].

A thorough summary of current research on ESC system

development techniques in EVs can be found in Table 7.

Although the table presents novel strategies to improve the

effectiveness and functionality of ESC, there are signiﬁcant

factors that require taking into account. Firstly, handling

stability and motor efﬁciency are signiﬁcantly improved in

the study using a torque distribution strategy based on Deep

Reinforcement Learning (DRL) [120]. Future improvements

will likely be necessary because of the computing demands

of large-scale implementations, which might provide difﬁcul-

ties. Secondly, it is noteworthy that Phase Change Material

(PCM) has been included in Battery Thermal Management

Systems (BTMS) to improve thermal efﬁciency [121]. How-

ever, long-term dependability issues with PCM deterioration

and ideal material combinations indicate areas that need more

investigation. Lastly, to maximize energy efﬁciency without

sacriﬁcing perception performance, Adaptive Sensor Fusion

with Energy Awareness for object detection in autonomous

vehicles (AVs) presents a potential method [122]. However,

given the model’s restricted application to particular settings,

more research should be conducted using comparative assess-

ments and thorough data-driven analysis. These important

considerations highlight areas where ESC techniques for EVs

should be worked upon to advance the research in this ﬁeld

for further energy efﬁciency improvement.

C. ESC ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY

ESC is a safety system in EVs designed to help drivers

maintain control of their vehicles during emergencies or in

hazardous road conditions [123]. ESC works by detecting and

reducing instances of oversteer or understeer, which occur

when a vehicle loses traction with the road surface, caus-

ing it to spin or slide out of control. In an electric vehicle,

ESC can also play a role in improving energy efﬁciency

and range [124]. By monitoring the vehicle’s stability and

reducing unnecessary acceleration or deceleration, ESC can

help prevent wasteful energy consumption and extend the

battery’s range. To provide stability, the ESC monitors the

yaw rate and steering wheel sensors to detect a lack of steering

control. Once detected, the ESC activates the ABS (Antilock

Braking System) and ECM (Engine control module) to reduce

the vehicle’s speed. The ESC system can detect when one or

more wheels are slipping and apply the brakes or adjust the

power to the motor to help maintain traction and prevent the

vehicle from skidding or spinning out of control. The ESC

system can detect when the vehicle is starting to oversteer or

understeer and apply the brakes or power adjustment of the

motor to help keep the vehicle on the intended path [125]. The

ESC system can detect when the vehicle is at risk of rolling

over and apply the brakes or adjust the power supply to the

motor to help prevent the vehicle from rolling over [126]. The

ESC system can also hold the brakes for a short period when

the vehicle is stopped on a hill, allowing the driver to start

the vehicle without rolling backward [127]. Table 8 shows

the ESC control algorithms for mobility energy efﬁciency in

EVs along with their strength and limitations.

VI. DEVELOPMENT STRATEGIES AND ALGORITHMS OF

BCU FOR MOBILITY ENERGY EFFICIENCY

A. BCU CONFIGURATION AND SENSORS

The body control unit (BCU) in electric vehicles incorpo-

rates various sensors to monitor and control different systems

within the vehicle’s body, as shown in Figure 13. These sen-

sors enhance the occupants’ safety, convenience, and comfort.

Some of the key sensors used in the BCU of electric vehicles

include those for lighting control, windshield wipers, power

windows, door locks, and the HVAC (Heating, Ventilation,

and Air Conditioning) system [135].

Furthermore, lighting sensors enable the automatic control

of lighting systems, such as automatic headlights and interior

lighting. These sensors detect ambient light levels and adjust

the vehicle’s lighting accordingly, enhancing visibility and

reducing driver distraction. Windshield wiper sensors detect

rain or moisture on the windshield and activate the wipers

automatically. These sensors ensure optimal visibility for the

driver, enhancing safety during inclement weather conditions.

Power window sensors detect obstacles in the window’s path,

preventing injuries and damage. They automatically stop the

window movement if an obstruction is detected, ensuring the

safe operation of the power windows [136].

Similarly, door lock sensors are vital to the vehicle’s se-

curity. They detect whether the doors are open or closed and

14 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 7. Latest Research on Development Strategies of ESC in EV

Ref. Development

Strategy

Description Merits Demerits

[120]

Torque Distribution

Strategy Based on

Deep Reinforcement

Learning (DRL)

DRL-based approach for

torque distribution in EVs.

Torque distribution is a

decision-making process that

incorporates motor efﬁciency

in cumulative reward.

Better Handling Stability: The proposed

strategy signiﬁcantly lowers energy loss in

the range of 5.25% to 10.51% during typical

steering motions and greatly increases aver-

age motor efﬁciency.

Large-scale computational capability:

Optimization of algorithms can be done

in the future for more effective compu-

tation

[121]

Phase change material

(PCM) assisted bat-

tery thermal manage-

ment system (BTMS)

The study proposed the integra-

tion of jute ﬁbers with PCM-

based cooling techniques to in-

crease the thermal efﬁciency of

BTMS in EVs

Enhanced Thermal Efﬁciency: Compared

to conventional cooling techniques, the pro-

posed novel strategy produces noticeably

lower maximum temperatures, indicating im-

proved thermal efﬁciency

Long-term Reliability: Interior perfor-

mance of PCM, degradation of jute

ﬁbers, and determining the optimal

number of jute layers can be studied as

the future work for developing robust

and durable BTMS

[122]

Adaptive Sensor

Fusion with Energy

Awareness for

object detection in

autonomous vehicles

(AVs)

The proposed method opti-

mizes energy usage without

sacriﬁcing perception perfor-

mance by adapting the fusion

mechanism based on circum-

stances.

Improved Efﬁciency with Reduced En-

ergy Utilization: The suggested approach

performs up to 9.5% better than the current

sensor fusion techniques while using around

60% less energy and reducing latency by

58%.

Restricted Applicability: The study

concentrates on particular scenarios or

circumstances. To improve the ﬂexibil-

ity of the proposed model, future work

could involve comparative evaluations

and data-driven analysis

TABLE 8. Control Algorithms of Electronic Stability Control in Electric Vehicles

Ref. Control Algorithm Description Strength Limitation

[128],

[129]

Anti-lock Braking

System (ABS)

Control

ABS control is a fundamental component of ESC sys-

tems that modulates the braking force at each wheel

to prevent wheel lock-up during braking

Prevents wheel lock-up during

braking, improves stability and

control of EV

Low capability to address lat-

eral stability issues, does not

actively involve in vehicle yaw

motion

[130],

[131]

Traction Control

System (TCS)

Control

TCS control is designed to prevent wheel slip during

acceleration by utilizing wheel speed sensors to mon-

itor individual wheel speeds and modulates the power

or torque delivered to the wheels to optimize traction

Improve the traction and stabil-

ity control during acceleration,

avoid excessive wheel spin and

loss of control

Focuses primarily on accelera-

tion and traction, not lateral sta-

bility, limited control over yaw

motion

[132],

[133]

Roll Stability Con-

trol (RSC)

Active return control is an EPS algorithm that auto-

matically assists the driver in returning the steering

wheel to its neutral position after completing a turn

Improves vehicle stability, pro-

vides smoother steering inputs

Highly reliable response from

actuators, more complex EPS

system with higher cost.

[134] Hill Start Assist

Control (HSAC)

HSAC is an ESC algorithm that prevents a vehicle

from rolling backward when starting on an incline by

holding the brake pressure for a short period, allowing

the driver to smoothly transition from the brake pedal

to the accelerator without the vehicle rolling backward

Enhances driver convenience

and control, reduces the like-

lihood of accidents during hill

starts

Limited functionality speciﬁ-

cally for hill start situations, re-

quires accurate detection and

control of the slope gradient.

provide feedback to the BCU. This information is used for

various functions, such as automatically locking the doors

when the vehicle is in motion or unlocking the doors when

the vehicle is parked. The HVAC system sensors monitor

parameters like temperature, humidity, and cabin occupancy.

They enable the BCU to control the HVAC system effectively,

maintaining a comfortable environment inside the vehicle.

These sensors work with the BCU to provide intelligent con-

trol over various body systems in electric vehicles, ensuring

enhanced safety, convenience, and comfort for the occupants.

B. DEVELOPMENT STRATEGIES OF BCU FOR MOBILITY

ENERGY EFFICIENCY

Different development strategies are presented in Figure 14

for a BCU to improve mobility energy efﬁciency. These

strategies include power management, regenerative braking,

lightweight design, and V2V communication. The BCU can

manage the power consumption of various electronic systems

in the vehicle by turning them off when not in use. For

FIGURE 13. Basic configuration of BCU using input sensors and output

controls

example, the BCU can turn off the climate control system

when the vehicle is not occupied [137]. Advanced thermal

control systems can be designed to guarantee that the BCU

operates within ideal temperature ranges, hence lowering

VOLUME 11, 2023 15

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

the requirement for energy-intensive cooling systems and

reducing the load on the electrical system by utilizing the

thermal energy produced within the car for heating. [73]. The

BCU can be designed to communicate with other vehicles

on the road. This can allow for more efﬁcient driving by

sharing information about trafﬁc conditions and optimizing

the operation of electronic systems in the vehicle [138].

Table 9 comprehensively describes current research on

the development approaches used for BCUs in EVs. These

strategies demonstrate creative methods for improving energy

efﬁciency and maximizing vehicle performance. The study

introducing the Long Short-Term memory-based improved

Model Predictive Control algorithm (LSTM-IMPC) showed

promising results in increasing fuel efﬁciency, ultimately

resulting in improved energy efﬁciency [139]. However, it’s

crucial to remember that the algorithm solely addresses in-

ternal vehicle energy management; real-time sensor data and

signiﬁcant external elements are not included, indicating a

potential area for future research. Additionally, it is notewor-

thy that Nonlinear Model Predictive Control (NMPC) is used

for cabin cooling systems since it may increase efﬁciency

while lowering energy consumption [140]. However, there

is still a need for more research and development into how

resistant the NMPC model is to uncertainties such as un-

modeled dynamics and disturbances. Furthermore, fuel con-

sumption is signiﬁcantly reduced by the Multi-Agent Rein-

forcement Learning (MARL) based optimal energy-saving

strategy for Hybrid Electric Vehicles (HEVs) presented by

[141]. Nonetheless, optimization and practical application

issues may impact the method’s adaptability, which can be

considered for future research. These considerations under-

score possible paths for future study in BCU techniques in

EVs and emphasize improving these strategies to guarantee

their efﬁcacy in real-world circumstances.

FIGURE 14. Taxonomy of development strategies for BCU

C. BCU ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY

The body control unit (BCU) is a component in electric vehi-

cles (EVs) that controls the various electronic systems in the

vehicle, including lighting, climate control, and the locking

system. By optimizing the operation of these systems, the

BCU can improve the vehicle’s energy efﬁciency. The BCU

controls the exterior and interior lighting systems, including

headlights, taillights, brake lights, turn signals, and interior

lights [142]. The BCU controls the windshield wipers, includ-

ing the speed and frequency of the wipers. The BCU controls

the power windows’ operation, including the windows’ open-

ing and closing. The BCU controls the operation of the door

locks, including the locking and unlocking of the doors [135].

Besides, the BCU controls the heating, ventilation, and air

conditioning (HVAC) system, including the temperature, fan

speed, and airﬂow [143]. Table 10 illustrates the BCU control

algorithms for mobility energy efﬁciency in EVs, mentioning

their strength and limitations.

VII. COMPARISON OF INTEGRATED ECU ALGORITHMS

FOR MOBILITY ENERGY EFFICIENCY

Table 11 presents a critical analysis of existing control ap-

proaches for energy efﬁciency in EVs using regenerative

braking. Different factors are considered to analyze existing

control strategies to highlight the research gap critically. The

factors incorporate the type of control method used, the main

objective of the proposed study, strengths, and limitations.

In [148], Adeleke et al. presented an in-depth torque dis-

tribution approach for four in-wheel motor drive EVs. With

an emphasis on energy efﬁciency, they use the dynamic pro-

gramming (DP) technique to optimize the torque distribution

between the front and rear in-wheel motors. The suggested

DP algorithm has been shown through thorough modeling and

experimental research to dramatically reduce the vehicle’s

energy consumption across various driving cycle situations,

demonstrating notable increases in energy efﬁciency. It’s

important to remember that DP necessitates more time and

space due to its comprehensive computations. Future areas

for research need to concentrate on reﬁning the method to

augment computational effectiveness while maintaining the

capacity for real-time monitoring.

In [149], Lee et al. proposed a model to reduce energy

consumption in autonomous EVs by optimizing speed in

various driving circumstances. The model-based Reinforce-

ment Learning (RL) method is used for eco-driving. The

RL algorithm outperformed the global optimum solution by

93.8% using vehicle powertrain dynamics. Future work could

focus on experiments in the real world and more valida-

tion under various driving circumstances. Reducing algorithm

complexity and increasing convergence speed via techniques

like transfer learning is crucial for improving practical imple-

mentation, which can be explored in future studies.

In [150], Pei et al. proposed an efﬁciency optimiza-

tion strategy for EV motors, enhancing efﬁciency by 4-7%

through a novel Loss Minimization Algorithm (LMA) based

on the motor’s energy balance equation. The LMA adjusts

excitation current, improving efﬁciency during steady-state

operations. However, the method assumes constant motor

parameters, limiting its applicability. Real-world experiments

16 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 9. Latest Research on Development Strategies of BCU in EV

Ref. Development

Strategy

Description Merits Demerits

[139]

Long short-term

memory based

improved model

predictive control

algorithm (LSTM-

IMPC)

Proposed an LSTM-IMPC al-

gorithm for the energy man-

agement strategy in plug-in hy-

brid electric vehicles (PHEV)

and evaluated performance in

comparison to rule-based tech-

niques

Enhanced Fuel Efﬁciency: The proposed

model shows considerable fuel-saving rates

of 3.81%, 5.6%, and 18.71% over the tradi-

tional energy management systems

External Factors and Real-Time

Data: The proposed EMS addresses

internal vehicle energy management,

ignoring external variables and real-time

data from sensors which could be added

in future work

[140]

Nonlinear Model

Predictive Control

(NMPC) for Cabin

Cooling System

The proposed model ensures

thermal comfort while consum-

ing the least amount of energy

by optimizing the air mass ﬂow

trajectories and cabin inlet air

temperature

Improved Efﬁciency with Reduced En-

ergy Utilization: The suggested approach

performs up to 9.5% better than the current

sensor fusion techniques while using around

60% less energy and reducing latency by

58%.

Robustness and Real-World Imple-

mentation: Robustness of the proposed

NMPC has uncertainty concerns like

unmodeled dynamics and disturbances,

which could be explored in the future to

improve the NMPC’s robustness

[141]

Multi-Agent

Reinforcement

Learning (MARL)

based optimal energy-

saving strategy for

Hybrid Electric

Vehicle (HEV)

The proposed model enables

cooperative control of the en-

gine and the car-following be-

haviors by actively reducing

energy consumption.

Signiﬁcant Fuel Consumption Reduction:

The paper’s proposed MARL model demon-

strates fuel consumption reduction by 15.8%

compared to the hierarchical Model Predic-

tive Control (MPC) strategy, ensuring signif-

icant advancement in energy efﬁciency

Enhancing Algorithm’s Adaptability:

The suggested MARL algorithm could

face challenges in optimization and real-

world applicability, which can be im-

proved in future research

TABLE 10. Control Algorithms of Body Control Unit in Electric Vehicles

Ref. Control Algorithm Description Strength Limitation

[144] Lighting Control Lighting control algorithms in the BCU manage var-

ious lighting functions, including exterior lighting,

interior lighting, and adaptive lighting systems

Automated lighting control for

enhanced safety, optimizes en-

ergy efﬁciency by adjusting

lighting levels

Requires accurate sensor inputs

for optimal performance, vul-

nerable to sensor malfunction-

ing

[145] Wiper Control Wiper control algorithms in the BCU regulate the op-

eration and speed of windshield wipers based on input

from rain sensors, vehicle speed, and user settings

Automated wiper control for

improved visibility and safety,

adapt wiper operation to vary-

ing rain intensities

Relies on accurate rain sensor

inputs for optimal control, re-

quires ﬁne-tuning for different

weather conditions

[146] Power Windows

Control

Power windows control algorithms in the BCU man-

age the operation and positioning of the vehicle’s

power windows

Precise control over

power window operation,

incorporates anti-pinch

functionality for user safety

Requires reliable sensor in-

puts for anti-pinch functional-

ity, vulnerable to motor or sen-

sor malfunctioning

[147] HVAC Control HVAC control algorithms in the BCU regulate the

operation of the Heating, Ventilation, and Air Condi-

tioning system in the vehicle

optimize energy efﬁciency by

adjusting HVAC parameters as

needed

rely on accurate sensor inputs

for optimal control and vulner-

able to sensor malfunctioning

and parameter variations, such as temperature, are necessary

for comprehensive validation and practical implementation in

diverse EV scenarios, which can be researched in the future.

In [151], Nassar et al. developed multi-objective energy

management strategies for pre- and post-transmission par-

allel hybrid electric vehicles (HEVs). These strategies use

genetic algorithms to optimize fuel consumption, electric

system efﬁciency, and battery life. Results show improved

battery performance and varied effects on fuel consumption

compared to a baseline strategy. However, the study’s lim-

ited applicability requires further validation, and real-time

implementation complexities suggest the need for adaptive

algorithms in future research.

In [152], Mehbodniya et al. investigated optimization

methodologies for three-phase induction motors, emphasiz-

ing ﬁeld-oriented control and direct torque control tech-

niques. The study shows improved performance and energy

efﬁciency using fractional order Darwinian particle swarm

optimization (FODPSO), especially for the direct torque con-

trol method. Nevertheless, comprehensive comparison as-

sessments of different optimization strategies should be in-

cluded in the work. Future work should incorporate com-

prehensive performance comparisons and real-world appli-

cations to facilitate decision-making in the real world effec-

tively.

In [153], Xu et al. investigate deep reinforcement learning-

based energy management strategy (EMS) for hybrid electric

vehicles (HEVs). The study investigates how deep reinforce-

ment learning (DRL) approaches might be applied to trans-

fer learning. It evaluates several exploration methods inside

the Deep Deterministic Policy Gradient (DDPG) algorithm

architecture, such as introducing noise into the parameter and

action spaces. The ﬁndings show that, in comparison to the

combination of parameter space noise and action space noise,

VOLUME 11, 2023 17

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

adding parameter space noise in the network is more stable

and converges faster for transferable EMS. One limitation

of the study is that it investigates only the noise addition

technique. It leaves up the potential for future research by

investigating a wider variety of exploratory approaches with

robust DRL algorithms to enhance EMS in HEVs.

In [154], Zhang et al. proposed and simulated a hierar-

chical model predictive control algorithm for the thermal

management of EV by using the energy restored through

regenerative braking. The upper layer MPC controller opti-

mally plans the vehicle’s speed, while the lower layer focuses

on implementing thermal management strategies based on

the planned regenerative braking for energy efﬁciency. By

conducting experiments, the proposed method demonstrates

a reduction in energy consumption by 3.38% and a decrease

in battery aging of up to 29.15% when compared to the

benchmark method. The study may have focused on a speciﬁc

vehicle or air conditioning system, and the ﬁndings may not

apply to other vehicle types or HVAC (heating, ventilation,

and air conditioning) systems. The study may have yet to

fully consider the impact of other factors, such as weather

conditions or driver behavior. The cost and complexity of

retroﬁtting existing vehicles with the necessary equipment to

implement the proposed strategy may be prohibitively high,

limiting its adoption.

In [155], Satzger et al. introduce a predictive braking con-

trol method for EVs that uses a predictive control framework

to optimize energy recuperation and wheel slip regulation.

The algorithm is designed for EVs with redundant braking

actuators. The algorithm was experimented on the ROboMO-

bil (ROMO) research vehicle, and results showed up to a 60

percent reduction in torque tracking error and a 10 percent

improvement in emergency deceleration compared to other

control techniques.

There could be several limitations to this study. One limi-

tation could be the speciﬁc experimental setup used for val-

idation. The ROboMObil (ROMO) research vehicle used in

the study may only represent some types of electric vehicles,

which could limit the generalizability of the ﬁndings. Addi-

tionally, the proposed algorithm’s effectiveness may depend

on speciﬁc driving conditions and scenarios not explored in

the study. Finally, the cost and complexity of implementing

the proposed algorithm in a production vehicle may also limit

its practical application.

In [156], Chen et al. proposed a hierarchical framework for

regenerative braking control in independently operated EVs.

The upper-layer sliding mode controller (SMC) estimates

vehicle states by a modular observer, which tracks desired

velocity proﬁles. After calculating the overall braking torque,

a lower-layer controller uses a control allocation algorithm

to split the braking torque between the front and rear wheels

as efﬁciently as possible to maximize energy recovery, ulti-

mately improving EV efﬁciency. Although useful, the study’s

dependence on certain tire characteristics for velocity esti-

mation has limitations and points to the need for adaptable

methodologies to be explored in the future.

In [157], Kousalya et al. proposed the implementation of

predictive torque control (PTC) as an energy-saving approach

for the motor used in EV. The PTC method was used to

overcome the torque ripple problem in conventional direct

torque control (DTC). The electric motor’s torque ripple and

speed response in EVs are analyzed across various operating

modes. This examination aims to generate an energy-saving

plan designed for the electric motor. The paper’s limitation

lies in the high computational burden, dependence on sys-

tem models, and challenges in tuning weighting factors for

Predictive Torque Control (PTC). Future studies could fo-

cus on developing efﬁcient computational methods, adaptive

algorithms for changing parameters, and automated tuning

techniques to enhance PTC’s applicability in traction motors.

In [158], Zerd et al. proposed a novel speed-sensorless

ﬁnite control set-predictive torque control for EVs utiliz-

ing induction motor (IM) drives. The method combines the

advantages of IM with the ability to handle nonlinearities

and eliminates speed sensors, increasing reliability and re-

ducing costs. An adaptive fading extended Kalman ﬁlter is

introduced to approximate load torque and enhance torque

response. The proposed method shows improved control per-

formance, making it a promising solution for electric vehicle

propulsion systems. One potential limitation of the research

study is that the proposed method may have limitations in

high-speed applications due to the ﬁnite control set and

prediction errors. Moreover, the proposed method requires

an exact approximation of the load torque, which can be

challenging in some operating conditions. Additionally, the

experimental studies were performed on a small-scale motor,

and further research may be required to validate the proposed

method on larger-scale motors and under different operating

conditions. Finally, the cost and complexity associated with

implementing the adaptive fading extended Kalman ﬁlter may

be a limitation for practical applications.

In [159], Vajedi et al. introduced an ecological adaptive

cruise control (Eco-ACC) system for plug-in hybrid EVs that

optimizes fuel economy and safety by utilizing nonlinear

MPC to adjust vehicle speed based on the surrounding en-

vironment and adapts its driving behavior accordingly. The

developed model is tested and compared with PID and linear

MPC controllers in simulations. The ﬁndings demonstrate

that utilizing Nonlinear Model Predictive Control signiﬁ-

cantly improves total energy cost, with an increase of up to

19 percent. while maintaining vehicle safety. The Eco-ACC’s

performance limitations under speciﬁc trafﬁc conditions and

the critical inﬂuence of the prediction horizon parameter

highlight the necessity for future research in adaptive systems.

Developing advanced algorithms for autonomous prediction

horizon optimization based on real-time trafﬁc data can en-

hance Eco-ACC adaptability and efﬁciency across diverse

driving scenarios.

In [160], Zhu et al. presented an MPC approach for EVs to

accurately track a desired speed proﬁle while ensuring safety

and stability. The proposed approach uses a kinematic model

to predict the vehicle’s future behavior and adjust control in-

18 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

TABLE 11. Comparison of Integrated ECU Algorithms related to EV control

Ref. Control Method Objective Strength Limitation

[148] Dynamic Programming Con-

trol Algorithm

Optimal Torque Distribution and

energy efﬁciency for four in-

wheel motor drive (4IWMD) EV

Reduced energy consumption

by 23.01% (IM240), 23.12%

(NEDC), and 23.89% (WLTC).

Exhaustive nature of the Dynamic

Programming (DP) algorithm

demands signiﬁcant computational

and time resources

[149] Model-Based Reinforcement

Learning (RL)

Reduce energy consumption by

optimizing vehicle speed under a

range of driving scenarios

Near-optimal performance of

93.8% is attained

RL algorithm’s complexity and con-

vergence time need to be improved

[150] New Loss Minimization Algo-

rithm (LMA)

Efﬁciency optimization of per-

manent magnet synchronous mo-

tor (PMSM) for EVs

Improved efﬁciency by about 4-

7% compared with the conven-

tional LMA methods

Limited to the steady-state opera-

tion of PMSM

[151] Multi-objective Genetic Algo-

rithm

Improvements in efﬁciency for

the electric system, battery life,

and fuel economy

signiﬁcant increase in battery ef-

ﬁciency resulting in enhanced en-

ergy management strategy appli-

cable to diverse driving scenarios

Complex ofﬂine computations for

various driving conditions with re-

sults storage in optimum look-up ta-

bles

[152] Fractional Order Darwinian

Particle Swarm Optimization

(FODPSO)

FODPSO based Field oriented

control (FOC) to optimize motor

performance and improve energy

efﬁciency

Proposed model outperforms tra-

ditional Field oriented control

(FOC) and Direct torque control

(DTC) methods in terms of efﬁ-

ciency

Limited performance comparison

with other optimization techniques

[153] Deep reinforcement learning

(DRL) combined with transfer

learning

To ﬁnd the most effective and op-

timal energy management strat-

egy (EMS) for hybrid electric ve-

hicles (HEVs)

Transfer learning algorithm en-

hances the efﬁciency of EMS se-

lection

Only one exploration method (i.e,

adding noise for action selection) of

DRL is considered.

[154] Hierarchical MPC (model pre-

dictive control) strategy

Minimization of battery degrada-

tion to extend its life

The developed model achieves en-

ergy savings of 3.38% and signif-

icantly reduces battery aging by

29.15%

The hierarchical MPC control strat-

egy is not suited for cabin thermal

management scenario in winter

[155] Model predictive control

(MPC)

Predictive braking control ap-

proach for EVs is presented

The torque tracking error is re-

duced by up to 60%, while there

is an improvement of up to 10%

in deceleration during emergency

braking

MPC can be made more robust

with online algorithms for estimat-

ing tire-road friction and optimal

wheel-slip

[156] Modular observer combined

with hierarchical feedback

control

Control of regenerative braking Stability of the proposed control

algorithm is analyzed using input-

to-state stability theory

Reliance on longitudinal tire forces

for velocity estimation.

[157] Predictive torque control (PTC) Energy saving for electric mo-

tor with decreased motor copper

losses

Outperforms DTC with reduced

torque ripple, lower Total har-

monic distortion (THD) in stator

current, less noise, and improved

speed tracking accuracy

Challenges in tuning weighting fac-

tors for Predictive Torque Control

(PTC)

[158] Speed-sensorless ﬁnite control

set-predictive torque control

(FSC-PTC)

Improvement in the control per-

formance of the IM drive by esti-

mating the load torque

Improved control performance in

various operating conditions by

combining adaptive fading ex-

tended Kalman ﬁlter with the pro-

posed control technique

Proposed method may have limita-

tions in high-speed applications due

to the ﬁnite control set.

[159] Ecological Adaptive Cruise

Control (ECO-ACC) with

Non-linear Model Predictive

Control (NMPC)

Fuel economy and safety by op-

timally adjusting vehicle speed

based on upcoming trip data

Improve in energy cost to about

19%

Careful tuning of prediction horizon

parameter for optimal performance

across diverse driving conditions.

[160] Model predictive control Implementation of speed control

strategy based on MPC

Safe and stable speed tracking

with minimum error while consid-

ering the constraints

Only the speed tracking aspect of

autonomous driving is considered

avoiding other aspects which may

affect the proposed system perfor-

mance.

[161] Model predictive control

(MPC)

Minimize the battery aging while

maintaining good braking perfor-

mance

Effective management of the bat-

tery current and braking force dis-

tribution

The proposed strategy is not tested

for different driving styles or road

conditions

[162] Parameterized model predic-

tive control

Steering performance improve-

ment of EVs

Real-time optimization of control

inputs are employed to enhance

the steering performance of EVs.

Sensitivity to prediction horizon re-

quires precise tuning for real-world

applications.

[163] Global search algorithm com-

bined with Model predictive

controller (MPC)

Improvement in energy

efﬁciency of in-wheel motors

through optimal torque

distribution

Reduces energy consumption by

21.66% and 11.18% in speciﬁc

maneuvers; 10.13% in slalom test

Using only the wheel slip ratio to

optimize torque.

[164] Modular Optimal Control

(High-Level Model Predictive

Control and Low-Level Torque

Regulation)

Integrated Longitudinal and Lat-

eral Vehicle Stability to prevent

real-time accidents

The proposed system enhances

vehicle stability in challenging

driving maneuvers compatible

with various actuation systems

Limited validation on vehicles with

active steering and differential brak-

ing systems.

VOLUME 11, 2023 19

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

puts accordingly. Simulation results validate the effectiveness

of the proposed approach in achieving the desired speed pro-

ﬁle with minimal tracking error and smooth control actions.

The proposed approach only considers speed tracking and

does not address other aspects of autonomous driving, such

as path planning, obstacle avoidance, and decision-making.

The approach may be sensitive to the model’s parameters and

assumptions, affecting its performance in different scenarios.

In [161], Wu et al. introduced a hierarchical control strat-

egy for regenerative braking in hybrid electric vehicles con-

sidering battery aging. The strategy involves two levels of

control: a high-level controller that determines the braking

force distribution and a low-level controller that regulates the

battery current. The proposed approach is evaluated through

experimentation and simulation, and the results afﬁrm that it

can effectively prolong the battery lifespan while maintaining

good braking performance. This study has a few areas for

improvement in that it focuses solely on the battery aging

aspect of the regenerative braking system and does not con-

sider other factors that could affect the battery lifespan, such

as temperature and cycling rate. Also, this study assumes

a speciﬁc driving scenario. It does not consider the effects

of different driving styles or road conditions, which could

impact the performance of the regenerative braking system

and the battery’s lifespan.

In [162], Murilo et al. proposed a parameterized model

predictive control approach for the electric power-assisted

steering system, enabling tuning control parameters to meet

different performance objectives and handle various operat-

ing conditions. The approach relies on the Electric Power

Assisted Steering (EPAS) system model, utilizing real-time

optimization of control inputs to enhance the steering perfor-

mance of EVs. The simulation results demonstrate that the

proposed control strategy can outperform traditional control

methods, indicating its potential to enhance the overall steer-

ing performance of EPAS systems. Future research might im-

prove the prediction horizon selection procedure to improve

the parameterized MPC’s robustness. Furthermore, doing a

ﬁeld test on a vehicle will yield important information on how

well the suggested control technique works in actual EPAS

systems.

In [163], Changqing et al. suggested an integrated control

mechanism for EVs using active front steering and MPC

control. The strategy aims to enhance the vehicle’s stability,

energy efﬁciency, and driving comfort by coordinating torque

distribution between front and rear wheels and adjusting the

steering angle. Simulations show that the proposed strategy

surpasses traditional handling and energy consumption con-

trol. The results indicate that an integrated approach can

enhance the overall performance of EVs. One potential limi-

tation of this study is that it only considers improving driving

torque distribution based on the wheel slip ratio, potentially

ignoring other important aspects that signiﬁcantly impact

the vehicle’s performance and energy economy. Subsequent

investigations may examine a more thorough optimization

strategy considering other variables besides the wheel slip

ratio. This might result in a more comprehensive and sophis-

ticated control approach for EVs, considering environmental

parameters, vehicle speed, and road surface conditions.

In [164], Nahidi et al. introduced a modular optimal control

method to highlight the challenge of integrated lateral and

longitudinal stability control for EVs. The proposed system

utilizes high and low-level controllers to regulate yaw mo-

ment and longitudinal force, enabling real-time optimization

of torque distribution among the wheels. The experimental

tests on an electric Chevrolet Equinox show improved vehicle

stability and dynamic response. While the system exhibits

adaptability to various vehicles and compatibility with var-

ious actuation systems, a more thorough study should in-

clude a wider range of EVs and actuation systems. To ensure

practical application, future research should concentrate on

expanding the control structure to a larger range of vehicles

and actuation systems.

Furthermore, EV data is acquired from the Kaggle website

to investigate the hidden patterns and trends of the different

models of Tesla EV [165]. This way, different analysis meth-

ods, such as correlation, boxplot, and comparison analysis,

are adopted to investigate the EV data. Our analysis revealed

interesting insights into the relationship between the selected

performance metrics and energy efﬁciency of electric cars

from Tesla, as shown in Figure 15. Speciﬁcally, we inves-

tigated the correlation between the following variables: Ac-

celeration Time (AccelSec), Top Speed (TopSpeed_KmH),

Range (Range_Km), Fast Charge (KmH) and Energy Efﬁ-

ciency (Efﬁciency_WhKm).

FIGURE 15. Correlation of selected performance metrics and energy

efficiency of EVs

We employed the Pearson correlation coefﬁcient to quan-

tify the degree of correlation between variables. The Pearson

correlation coefﬁcient measures the linear relationship be-

tween two variables, ranging from -1 to +1. A value close to

+1 indicates a strong positive correlation, while a value close

20 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

to -1 indicates a strong negative correlation. A value close to

0 suggests a weak or no correlation.

To visually represent the correlations, a correlation

heatmap is generated as shown in Figure 16. The heatmap il-

lustrates the strength and direction of the correlations between

the variables of interest. The color gradient in the heatmap

helps intuitively identify the correlations’ magnitude. The

resulting correlation heatmap indicates that top speed has the

strongest positive correlation with energy efﬁciency, followed

by range and fast charge with a slightly lower correlation.

Acceleration time shows a negative correlation with efﬁ-

ciency, indicating that if acceleration time increases, then the

efﬁciency of the EV decreases. Furthermore, the correlation

analysis and heatmap provide valuable insights into the re-

lationship between key performance metrics and the energy

efﬁciency of electric cars from Tesla. These ﬁndings can as-

sist researchers, manufacturers, and policymakers understand

the factors inﬂuencing energy efﬁciency in electric vehicles.

The ﬁndings of the correlation analysis are summarized as

listed below:

•Acceleration Time (AccelSec) vs. Energy Efﬁciency

(Efﬁciency_WhKm): A moderate negative correlation

is observed between the acceleration time and energy

efﬁciency of electric cars (-0.38). This suggests that

vehicles with high acceleration time have lower energy

efﬁciency.

•Top Speed (TopSpeed_KmH) vs. Energy Efﬁciency

(Efﬁciency_WhKm): A positive correlation is observed

between the top speed of EVs and their energy efﬁciency

(0.36). This implies that vehicles with higher top speeds

exhibit higher energy efﬁciency.

•Range (Range_Km) vs. Energy Efﬁciency (Efﬁ-

ciency_WhKm): A moderate positive correlation is ob-

served between the range of electric cars and their energy

efﬁciency (0.31). This implies that vehicles with a longer

range tend to exhibit higher energy efﬁciency.

•Fast Charge (Km_H) vs. Energy Efﬁciency (Efﬁ-

ciency_WhKm): A positive correlation is observed be-

tween the fast charge and energy efﬁciency of EV (0.3).

This implies that vehicles with a fast charge exhibit

higher energy efﬁciency.

In addition to the correlation study results regarding rapid

charging, it is critical to understand the fundamental trade-

off of fast charging rates because they may result in a shorter

battery life. Xie et al. [166] provided a thorough analysis that

clariﬁed important variables in this trade-off. Their results

highlight the need for a balanced approach, as they show that

high-rate charging can have mechanical effects on battery

components, such as physical stresses and strains, thermal

runaway (a phenomenon marked by an uncontrollably high

battery temperature), and even loss of lithium inventory,

which denotes a gradual decrease in the amount of lithium

that is available in the battery cells. In [166], optimum charg-

ing algorithms for Li-ion batteries are examined to avoid

battery degradation and achieve the shortest charging interval.

FIGURE 16. Correlation Heatmap of selected performance metrics and

energy efficiency of EVs

Therefore, as we explore the beneﬁts of rapid charging for

energy efﬁciency, we must balance these advantages with

potential effects on battery life. This comprehensive approach

ensures that the development and utilization of fast charging

for electric vehicles (EVs) consider both performance en-

hancement and the long-term sustainability of the EV batter-

ies.

VIII. SHORTCOMINGS OF AVAILABLE SOLUTIONS AND

FUTURE DIRECTIONS

Considering the ﬁndings presented in our study on the de-

velopment strategies and algorithms of an integrated ECU

for mobility energy efﬁciency, it becomes apparent that sig-

niﬁcant progress has been made in the overall EV control,

including VCU, EPS, ESC, and BCU. Even with these de-

velopments, it is clear that every method and algorithm still

needs more advancement to guarantee a sustainable future for

the broad use of EVs.

•Shortcomings of Integrated ECU Development: De-

spite the integration of predictive analytics, machine

learning, thermal management, power electronics, and

energy recuperation techniques, real-time data process-

ing and accuracy in predictive modeling persist.

•Shortcomings of VCU Development: While advance-

ments in sensor integration, powertrain component op-

timization, and intelligent energy management have en-

hanced Vehicle Control Units (VCUs), issues concern-

ing the interoperability of different components and

standardized communication protocols still pose chal-

lenges.

•Shortcomings of EPS Development: Electric Power

Steering (EPS) systems have come a long way by in-

troducing high-efﬁciency motors and regenerative en-

ergy harvesting. Yet, achieving a balance between power

efﬁciency and steering precision, especially in diverse

driving conditions, remains a challenge.

VOLUME 11, 2023 21

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

•Shortcomings of ESC Development: While smooth

driving and intelligent sensor fusion have improved the

driving experience, thermal management and sensor ac-

curacy challenges during extreme driving scenarios need

to be addressed for enhanced safety and stability.

•Shortcomings of BCU Development: Power manage-

ment, thermal regulation, and V2V communication in

Battery Control Units (BCUs) have limitations, partic-

ularly in managing power distribution optimally and

ensuring seamless communication between vehicles.

The solutions of the problems mentioned above are sum-

marized in Table 12 below:

TABLE 12. Problems and Solutions in EV Control Research

Problems Solutions

Limited predictive ana-

lytics accuracy

Implementation of advanced machine

learning algorithms for predictive

modeling, along with integrating real-

time feedback loops for data validation, is

required.

Insufﬁcient real-time

data processing

Research on high-speed processors and

hardware accelerators for real-time data

processing and developing parallel com-

puting techniques needs to be researched.

Limited adaptability to

diverse driving condi-

tions

Research is required to implement AI-

based algorithms that analyze driving pat-

terns, weather conditions, and trafﬁc data

to develop adaptive energy management

systems.

Insufﬁcient cybersecu-

rity measures for VCU

communication

Strong encryption methods and authen-

tication procedures are needed to avoid

emerging risks and weaknesses and pro-

tect VCU communication networks.

Limited adaptive steer-

ing control technology

Future research must focus on creating

AI-based systems that use adaptive steer-

ing control, which modiﬁes responsive-

ness and sensitivity to assess driving be-

havior and road conditions in real-time.

Limited accuracy

in sensor fusion

algorithms

More research is needed to improve sensor

fusion algorithms, enable real-time data

integration from many sensors, and in-

crease sensor data interpretation and accu-

racy through machine learning techniques.

Inadequate thermal con-

trol in BCU components

By creating passive and active thermal

management systems suited to the in-

dividual requirements of BCU compo-

nents, researchers may investigate ef-

fective thermal insulation materials and

cooling strategies to control temperatures

within the BCU.

Focusing on the challenges mentioned above and working

on possible solutions to these problems can open potential

directions for future research to improve energy efﬁciency

and ensure seamless integration of all control units. By di-

recting research efforts toward reﬁning control strategies and

algorithms, researchers can contribute to EV systems’ overall

efﬁciency and safety, thus laying the groundwork for future

advancements in this vital aspect of integrated ECUs.

Moreover, it would be beneﬁcial to expand the analysis

by contrasting the conventional control units in non-EVs to

explore potential areas for research in EV control units. This

comparative analysis may highlight important differences or

similarities in the development processes, offering sugges-

tions for future advances across disciplines. Furthermore, as

this survey focuses on increasing energy efﬁciency, future

studies may examine the relationship between electrical con-

trol units and sustainability. An in-depth understanding of

the wider implications of EV control technology would be

possible by considering environmental factors and investigat-

ing how the ﬁndings contribute to more environment-friendly

electric mobility solutions.

IX. CONCLUSION

In this comprehensive study, we have examined and detailed

the development strategies and algorithms of integrated Elec-

tronic Control Unit (ECU) and their crucial subsystems, in-

cluding Vehicle Control Unit (VCU), Electrical Power Steer-

ing (EPS), Electronic Stability Control (ESC), and Body Con-

trol Unit (BCU). By thoroughly examining different develop-

ment strategies and algorithms for each control unit, we have

highlighted their importance in attaining efﬁcient control in

EVs, ultimately resulting in improved energy efﬁciency.

Our research has emphasized the paramount importance

of an integrated ECU in optimizing the performance and

efﬁciency of diverse vehicle systems. The integrated ECU

enables energy management, reﬁnes vehicle dynamics, and

signiﬁcantly enhances overall energy efﬁciency by facilitat-

ing seamless communication and coordinated control actions

across subsystems. A vital aspect of our research includes

carefully examining sensors incorporated into every control

module. Precise control decisions are greatly aided by these

sensors, which record real-time data on acceleration, steering

angle, yaw rate, battery state of charge, and ambient factors.

By utilizing this data, the integrated ECU can optimize energy

use and increase the efﬁciency of the vehicle by making well-

informed modiﬁcations.

Additionally, our study has included a thorough review

of the state-of-the-art control methods presented in recent

research publications, providing a strong basis for our in-

vestigation and preparing for further research in this area.

The knowledge gathered from this in-depth analysis offers

researchers useful avenues to explore the ﬁeld of EV control

systems further, enabling continued progress in the hunt for

more energy-efﬁcient mobility solutions.

X. ACKNOWLEDGEMENT

SYED SHEHRYAR ALI NAQVI and FAIZA QAYYUM

contributed toward conceptualization, methodology, and

software. Furthermore, SYED SHEHRYAR ALI NAQVI,

HARUN JAMIL, and FAIZA QAYYUM performed a formal

analysis and prepared an original draft. Similarly, NAEEM

IQBAL, MURAD ALI KHAN, and Do-HYEUN KIM con-

tributed toward data curation, visualization, and supervision.

In addition, Salabat Khan reviewed and edited the original

draft and validated and investigated the overall manuscript.

The authors declared no conﬂict of interest regarding publish-

ing the role of Evolving Electric Mobility: In-Depth Analysis

of Integrated Electronic Control Unit Development in Electric

22 VOLUME 11, 2023

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

Vehicles. (SYED SHEHRYAR ALI NAQVI, HARUN JAMIL

and FAIZA QAYYUM contributed equally to this work.)

REFERENCES

[1] IEA IEA et al. Global ev outlook 2023. URL:

https://www.iea.org/reports/global-ev-outlook-2023, 2023.

[2] Madhav Kumar, Kaibalya Prasad Panda, Jayakrushna Moharana, Ritula

Thakur, and Gayadhar Panda. Hybrid energy source based bldc motor

drive for electric vehicle application. In 2023 2nd International Confer-

ence for Innovation in Technology (INOCON), pages 1–6. IEEE, 2023.

[3] Han Hao, Xiang Cheng, Zongwei Liu, and Fuquan Zhao. Electric vehicles

for greenhouse gas reduction in china: A cost-effectiveness analysis.

Transportation Research Part D: Transport and Environment, 56:68–84,

2017.

[4] International energy agency’s global ev data explorer.

https://www.iea.org/data-and-statistics/data-product/global-ev-outlook-

2023. [Online; accessed on 10-Oct-2023].

[5] Mohan Bharathidasan, V Indragandhi, Vishnu Suresh, Michał Jasiński,

and Zbigniew Leonowicz. A review on electric vehicle: Technologies,

energy trading, and cyber security. Energy Reports, 8:9662–9685, 2022.

[6] Ghulam E Mustafa Abro, Saiful Azrin BM Zulkiﬂi, Kundan Kumar,

Najib El Ouanjli, Vijanth Sagayan Asirvadam, and Mahmoud A Mossa.

Comprehensive review of recent advancements in battery technology,

propulsion, power interfaces, and vehicle network systems for intelligent

autonomous and connected electric vehicles. Energies, 16(6):2925, 2023.

[7] Sithara SG Acharige, Md Enamul Haque, Mohammad Tauﬁqul Arif,

Nasser Hosseinzadeh, Kazi N Hasan, and Aman Maung Than Oo. Review

of electric vehicle charging technologies, standards, architectures, and

converter conﬁgurations. IEEE Access, 2023.

[8] Madhav Kumar, Kaibalya Prasad Panda, Ramasamy T Naayagi, Ritula

Thakur, and Gayadhar Panda. Comprehensive review of electric vehicle

technology and its impacts: Detailed investigation of charging infras-

tructure, power management, and control techniques. Applied Sciences,

13(15):8919, 2023.

[9] Chuan Min, Yongjun Pan, Wei Dai, Ibna Kawsar, Zhixiong Li, and

Gengxiang Wang. Trajectory optimization of an electric vehicle with

minimum energy consumption using inverse dynamics model and servo

constraints. Mechanism and Machine Theory, 181:105185, 2023.

[10] Yisheng An, Yuxin Gao, Naiqi Wu, Jiawei Zhu, Hongzhang Li, and

Jinhui Yang. Optimal scheduling of electric vehicle charging operations

considering real-time trafﬁc condition and travel distance. Expert Systems

with Applications, 213:118941, 2023.

[11] Pranjal Barman, Lachit Dutta, Sushanta Bordoloi, Anamika Kalita, Pron-

amika Buragohain, Swapna Bharali, and Brian Azzopardi. Renewable

energy integration with electric vehicle technology: A review of the

existing smart charging approaches. Renewable and Sustainable Energy

Reviews, 183:113518, 2023.

[12] Farzad Rajaei Salmasi. Control strategies for hybrid electric vehicles:

Evolution, classiﬁcation, comparison and future trend. IEEE Transaction

on Vehicular Technology, 56(5):2393–2404, 2007.

[13] Zhengyu Yao and Hwan-Sik Yoon. Mild hybrid electric vehicle pow-

ertrain control using ofﬂine–online hybrid deep reinforcement learning

strategy. SAE International Journal of Electriﬁed Vehicles, 12(14-12-03-

0016), 2023.

[14] Federico Millo, Luciano Rolando, Luigi Tresca, and Luca Pulvirenti.

Development of a neural network-based energy management system for a

plug-in hybrid electric vehicle. Transportation Engineering, 11:100156,

2023.

[15] Ye Tao, Xiaohua Xie, Haiyan Zhao, Wei Xu, and Hong Chen. A regener-

ative braking system for electric vehicle with four in-wheel motors based

on fuzzy control. In 2017 36th Chinese Control Conference (CCC), pages

4288–4293. IEEE, 2017.

[16] Kerem Bayar, Junmin Wang, and Giorgio Rizzoni. Development of a

vehicle stability control strategy for a hybrid electric vehicle equipped

with axle motors. Proceedings of the Institution of Mechanical Engineers,

Part D: Journal of automobile engineering, 226(6):795–814, 2012.

[17] Amela Ajanovic. The future of electric vehicles: prospects and im-

pediments. Wiley Interdisciplinary Reviews: Energy and Environment,

4(6):521–536, 2015.

[18] Weiwen Deng, Yong H Lee, and Annie Zhao. Hardware-in-the-loop

simulation for autonomous driving. In 2008 34th Annual Conference of

IEEE Industrial Electronics, pages 1742–1747. IEEE, 2008.

[19] Yang Zhu, Hao Hu, Guoqing Xu, and Zhiguo Zhao. Hardware-in-the-loop

simulation of pure electric vehicle control system. In 2009 International

Asia Conference on Informatics in Control, Automation and Robotics,

pages 254–258. IEEE, 2009.

[20] Rabiatuladawiah Abu Hanifah, Siti Fauziah Toha, Salmiah Ahmad, and

Mohd Khair Hassan. Swarm-intelligence tuned current reduction for

power-assisted steering control in electric

Evolving Electric Mobility: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

Abstract